-----------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/davidbearce/log/PSRMlogfile.log
  log type:  text
 opened on:  17 Oct 2021, 19:48:10

. do "/var/folders/5b/1y16zyqn2hzf2txsyc9p18ph0000gn/T//SD34529.000000"

. *fn#8
. pwcorr Workersln FHinverted PolCon Polity CGV BMR, sig

             | Worker~n FHinve~d   PolCon   Polity      CGV      BMR
-------------+------------------------------------------------------
   Workersln |   1.0000 
             |
             |
  FHinverted |  -0.1511   1.0000 
             |   0.0000
             |
      PolCon |  -0.1217   0.7027   1.0000 
             |   0.0000   0.0000
             |
      Polity |  -0.1361   0.9021   0.7824   1.0000 
             |   0.0000   0.0000   0.0000
             |
         CGV |  -0.1355   0.8031   0.5940   0.8180   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
         BMR |  -0.0984   0.8860   0.6312   0.8583   0.8754   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

. *fn#10
. pwcorr Corruption FHinverted PolCon Polity CGV BMR, sig

             | Corrup~n FHinve~d   PolCon   Polity      CGV      BMR
-------------+------------------------------------------------------
  Corruption |   1.0000 
             |
             |
  FHinverted |  -0.6090   1.0000 
             |   0.0000
             |
      PolCon |  -0.3988   0.7027   1.0000 
             |   0.0000   0.0000
             |
      Polity |  -0.4518   0.9021   0.7824   1.0000 
             |   0.0000   0.0000   0.0000
             |
         CGV |  -0.3866   0.8031   0.5940   0.8180   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
         BMR |  -0.4272   0.8860   0.6312   0.8583   0.8754   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |

. *Table A1
. tab CountryYear

          CountryYear |      Freq.     Percent        Cum.
----------------------+-----------------------------------
          Albania2002 |        170        0.58        0.58
          Albania2005 |        204        0.69        1.27
          Armenia2002 |        171        0.58        1.85
          Armenia2005 |        351        1.19        3.04
       Azerbaijan2002 |        170        0.58        3.61
       Azerbaijan2005 |        350        1.19        4.80
          Belarus2002 |        250        0.85        5.65
          Belarus2005 |        325        1.10        6.75
            Benin2004 |        197        0.67        7.41
              BiH2002 |        182        0.62        8.03
              BiH2005 |        200        0.68        8.71
         Bulgaria2002 |        250        0.85        9.56
         Bulgaria2005 |        300        1.02       10.57
         Cambodia2003 |        503        1.70       12.28
            China2002 |      1,548        5.25       17.52
          Croatia2002 |        187        0.63       18.16
          Croatia2005 |        236        0.80       18.96
            Czech2002 |        268        0.91       19.86
            Czech2005 |        343        1.16       21.03
          Ecuador2003 |        453        1.54       22.56
          Estonia2002 |        170        0.58       23.14
          Estonia2005 |        219        0.74       23.88
          Georgia2002 |        174        0.59       24.47
          Georgia2005 |        200        0.68       25.15
          Germany2005 |      1,196        4.05       29.20
           Greece2005 |        546        1.85       31.05
          Hungary2002 |        250        0.85       31.90
          Hungary2005 |        610        2.07       33.96
          Ireland2005 |        501        1.70       35.66
       Kazakhstan2002 |        250        0.85       36.51
       Kazakhstan2005 |        585        1.98       38.49
            Kenya2003 |        284        0.96       39.45
       Kyrgyzstan2002 |        173        0.59       40.04
       Kyrgyzstan2003 |        102        0.35       40.38
       Kyrgyzstan2005 |        202        0.68       41.07
           Latvia2002 |        176        0.60       41.67
           Latvia2005 |        205        0.69       42.36
        Lithuania2002 |        200        0.68       43.04
        Lithuania2005 |        205        0.69       43.73
             Mali2003 |        155        0.53       44.26
        Mauritius2005 |        212        0.72       44.98
          Moldova2002 |        174        0.59       45.57
          Moldova2003 |        103        0.35       45.92
          Moldova2005 |        350        1.19       47.10
       NMacedonia2002 |        170        0.58       47.68
       NMacedonia2005 |        200        0.68       48.35
         Pakistan2002 |        965        3.27       51.62
             Peru2002 |        576        1.95       53.58
      Philippines2003 |        716        2.43       56.00
           Poland2002 |        500        1.69       57.70
           Poland2003 |        108        0.37       58.06
           Poland2005 |        975        3.30       61.37
         Portugal2005 |        505        1.71       63.08
          Romania2002 |        255        0.86       63.94
          Romania2005 |        600        2.03       65.98
           Russia2002 |        506        1.71       67.69
           Russia2005 |        601        2.04       69.73
          Senegal2003 |        262        0.89       70.61
Serbia&Montenegro2002 |        250        0.85       71.46
Serbia&Montenegro2003 |        508        1.72       73.18
Serbia&Montenegro2005 |        300        1.02       74.20
         Slovakia2002 |        170        0.58       74.78
         Slovakia2005 |        220        0.75       75.52
         Slovenia2002 |        188        0.64       76.16
         Slovenia2005 |        223        0.76       76.91
      SouthAfrica2003 |        603        2.04       78.96
       SouthKorea2005 |        598        2.03       80.98
            Spain2005 |        606        2.05       83.04
         SriLanka2004 |        452        1.53       84.57
       Tajikistan2002 |        176        0.60       85.16
       Tajikistan2003 |        107        0.36       85.53
       Tajikistan2005 |        200        0.68       86.21
         Tanzania2003 |        276        0.94       87.14
           Turkey2002 |        514        1.74       88.88
           Turkey2004 |        557        1.89       90.77
           Uganda2003 |        300        1.02       91.79
          Ukraine2002 |        463        1.57       93.36
          Ukraine2005 |        594        2.01       95.37
       Uzbekistan2002 |        260        0.88       96.25
       Uzbekistan2003 |        100        0.34       96.59
       Uzbekistan2005 |        300        1.02       97.60
          Vietnam2005 |        500        1.69       99.30
           Zambia2002 |        207        0.70      100.00
----------------------+-----------------------------------
                Total |     29,511      100.00

. *Table A2
. sum FHinverted PolCon Polity CGV BMR Corruption GDPpcln Market_Ageln Lobby LobbyBA BALobbies Influence Workersl
> n Firm_Ageln LocatedinCapital ForeignOwnership GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exportin
> g MNC Manufacturing Agriculture Construction Services Other

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  FHinverted |     29,511      7.7096     3.79905          0         12
      PolCon |     29,511    .3055648    .2043951          0   .7546894
      Polity |     29,511    14.75175    6.249791          1         20
         CGV |     29,511      .66687    .4713406          0          1
         BMR |     29,511    .6729355     .469149          0          1
-------------+---------------------------------------------------------
  Corruption |     29,511    63.56758    15.74101         18         82
     GDPpcln |     29,511    8.421241    1.217414   6.116329   10.84987
Market_Ageln |     29,511    3.316556     .734261   2.197225   4.077538
       Lobby |     29,352    .1539588    .3609154          0          1
     LobbyBA |     29,465    .3025963    .4593897          0          1
-------------+---------------------------------------------------------
   BALobbies |     29,295    .2139956    .4101307          0          1
   Influence |     12,073    .4693117    .8877937          0          4
   Workersln |     28,713    3.332791    1.645158          0   11.09733
  Firm_Ageln |     28,560     2.52428    .7687638          0   5.568345
LocatedinC~l |     28,963    .2923385    .4548449          0          1
-------------+---------------------------------------------------------
ForeignOwn~p |     29,410    10.68827    28.28795          0        100
GovtOwners~p |     29,409    8.379697     26.4486          0        100
 SalestoGovt |     27,171    5.683297    17.25526          0        100
PubliclyLi~d |     29,214     .068563    .2527139          0          1
DomesticIn~s |     28,194    72.41225    37.17427          0        100
-------------+---------------------------------------------------------
   Exporting |     29,470     .194944    .3961643          0          1
         MNC |     29,470    .0768578    .2663702          0          1
Manufactur~g |     28,738    .4563296    .4980979          0          1
 Agriculture |     28,738    .0204259    .1414546          0          1
Construction |     28,738    .0885239    .2840603          0          1
-------------+---------------------------------------------------------
    Services |     28,738    .4257081    .4944585          0          1
       Other |     28,738    .0090125    .0945068          0          1

. *Table A3
. logit LobbyNotMissing FHinverted Corruption GDPpcln, vce(cluster Country)

Iteration 0:   log pseudolikelihood = -989.12555  
Iteration 1:   log pseudolikelihood = -951.91365  
Iteration 2:   log pseudolikelihood = -942.43568  
Iteration 3:   log pseudolikelihood = -942.41955  
Iteration 4:   log pseudolikelihood = -942.41954  

Logistic regression                             Number of obs     =     29,511
                                                Wald chi2(3)      =      16.96
                                                Prob > chi2       =     0.0007
Log pseudolikelihood = -942.41954               Pseudo R2         =     0.0472

                                  (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------
                |               Robust
LobbyNotMissing |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
     FHinverted |     .08237   .1648622     0.50   0.617    -.2407538    .4054939
     Corruption |   .0362671   .0289256     1.25   0.210    -.0204261    .0929603
        GDPpcln |   .7129399   .2484209     2.87   0.004     .2260438    1.199836
          _cons |  -3.430197   3.050796    -1.12   0.261    -9.409648    2.549253
---------------------------------------------------------------------------------

. logit LobbyNotMissing PolCon     Corruption GDPpcln, vce(cluster Country)

Iteration 0:   log pseudolikelihood = -989.12555  
Iteration 1:   log pseudolikelihood = -944.97906  
Iteration 2:   log pseudolikelihood = -931.30788  
Iteration 3:   log pseudolikelihood = -931.25652  
Iteration 4:   log pseudolikelihood = -931.25651  

Logistic regression                             Number of obs     =     29,511
                                                Wald chi2(3)      =      16.66
                                                Prob > chi2       =     0.0008
Log pseudolikelihood = -931.25651               Pseudo R2         =     0.0585

                                  (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------
                |               Robust
LobbyNotMissing |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
         PolCon |   2.434578   1.880803     1.29   0.196    -1.251727    6.120883
     Corruption |   .0374093   .0264915     1.41   0.158    -.0145131    .0893316
        GDPpcln |   .7222176   .2086016     3.46   0.001      .313366    1.131069
          _cons |   -3.61157   3.294816    -1.10   0.273    -10.06929    2.846152
---------------------------------------------------------------------------------

. logit LobbyNotMissing Polity     Corruption GDPpcln, vce(cluster Country)

Iteration 0:   log pseudolikelihood = -989.12555  
Iteration 1:   log pseudolikelihood = -953.28361  
Iteration 2:   log pseudolikelihood = -942.14896  
Iteration 3:   log pseudolikelihood = -942.10047  
Iteration 4:   log pseudolikelihood = -942.10045  

Logistic regression                             Number of obs     =     29,511
                                                Wald chi2(3)      =      18.32
                                                Prob > chi2       =     0.0004
Log pseudolikelihood = -942.10045               Pseudo R2         =     0.0475

                                  (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------
                |               Robust
LobbyNotMissing |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
         Polity |    .043964   .0771199     0.57   0.569    -.1071881    .1951162
     Corruption |   .0315008   .0228875     1.38   0.169    -.0133578    .0763594
        GDPpcln |   .6961842   .2298381     3.03   0.002     .2457098    1.146659
          _cons |   -3.00287    2.71774    -1.10   0.269    -8.329542    2.323803
---------------------------------------------------------------------------------

. logit LobbyNotMissing CGV        Corruption GDPpcln, vce(cluster Country)

Iteration 0:   log pseudolikelihood = -989.12555  
Iteration 1:   log pseudolikelihood = -954.93954  
Iteration 2:   log pseudolikelihood =   -945.419  
Iteration 3:   log pseudolikelihood = -945.39293  
Iteration 4:   log pseudolikelihood = -945.39293  

Logistic regression                             Number of obs     =     29,511
                                                Wald chi2(3)      =      21.50
                                                Prob > chi2       =     0.0001
Log pseudolikelihood = -945.39293               Pseudo R2         =     0.0442

                                  (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------
                |               Robust
LobbyNotMissing |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
            CGV |   .3523428   .9529209     0.37   0.712    -1.515348    2.220034
     Corruption |    .031768   .0230006     1.38   0.167    -.0133124    .0768485
        GDPpcln |   .7569305   .1846315     4.10   0.000     .3950594    1.118802
          _cons |  -3.141672   2.524891    -1.24   0.213    -8.090367    1.807024
---------------------------------------------------------------------------------

. logit LobbyNotMissing BMR        Corruption GDPpcln, vce(cluster Country)

Iteration 0:   log pseudolikelihood = -989.12555  
Iteration 1:   log pseudolikelihood = -955.36227  
Iteration 2:   log pseudolikelihood =  -946.2638  
Iteration 3:   log pseudolikelihood =  -946.2403  
Iteration 4:   log pseudolikelihood =  -946.2403  

Logistic regression                             Number of obs     =     29,511
                                                Wald chi2(3)      =      21.88
                                                Prob > chi2       =     0.0001
Log pseudolikelihood =  -946.2403               Pseudo R2         =     0.0434

                                  (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------
                |               Robust
LobbyNotMissing |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
            BMR |   .2656588   1.058864     0.25   0.802    -1.809676    2.340994
     Corruption |   .0320993   .0231258     1.39   0.165    -.0132264    .0774251
        GDPpcln |   .7721141    .208463     3.70   0.000     .3635341    1.180694
          _cons |  -3.238499   2.402389    -1.35   0.178    -7.947096    1.470098
---------------------------------------------------------------------------------

. *Table 1
. melogit Lobby FHinverted Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -11439.03  
Iteration 1:   log likelihood = -11279.077  
Iteration 2:   log likelihood = -11278.291  
Iteration 3:   log likelihood =  -11278.29  

Refining starting values:

Grid node 0:   log likelihood = -10880.873

Fitting full model:

Iteration 0:   log pseudolikelihood = -10880.873  (not concave)
Iteration 1:   log pseudolikelihood = -10867.012  
Iteration 2:   log pseudolikelihood = -10860.494  
Iteration 3:   log pseudolikelihood = -10858.993  
Iteration 4:   log pseudolikelihood = -10858.993  

Mixed-effects logistic regression               Number of obs     =     28,617
Group variable:     CountryYear                 Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(12)     =     571.75
Log pseudolikelihood = -10858.993               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
           FHinverted |   .1225965   .0345113     3.55   0.000     .0549556    .1902374
           Corruption |   .0128395   .0111412     1.15   0.249    -.0089968    .0346759
              GDPpcln |  -.1880254   .1057343    -1.78   0.075    -.3952609      .01921
         Market_Ageln |  -.1968022    .163901    -1.20   0.230    -.5180423    .1244378
            Workersln |   .3940196   .0198318    19.87   0.000     .3551499    .4328893
      EastAsiaPacific |   .0519782   .4906549     0.11   0.916    -.9096878    1.013644
    EuropeCentralAsia |   .2873653   .4310651     0.67   0.505    -.5575067    1.132237
LatinAmericaCaribbean |   .3234207    .443181     0.73   0.466     -.545198    1.192039
            SouthAsia |   .8763318    .365296     2.40   0.016     .1603649    1.592299
                      |
                 Year |
                2003  |  -.6892255   .2864319    -2.41   0.016    -1.250622   -.1278293
                2004  |  -1.310891   .3966242    -3.31   0.001     -2.08826   -.5335221
                2005  |  -.5250999   .1166818    -4.50   0.000    -.7537921   -.2964078
                      |
                _cons |  -2.571712   1.443055    -1.78   0.075    -5.400048    .2566239
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3454099   .0584553                      .2479031    .4812687
---------------------------------------------------------------------------------------

. melogit Lobby PolCon     Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11459.487  
Iteration 1:   log likelihood = -11311.489  
Iteration 2:   log likelihood = -11310.859  
Iteration 3:   log likelihood = -11310.859  

Refining starting values:

Grid node 0:   log likelihood = -10882.841

Fitting full model:

Iteration 0:   log pseudolikelihood = -10882.841  (not concave)
Iteration 1:   log pseudolikelihood = -10869.299  
Iteration 2:   log pseudolikelihood =  -10862.22  
Iteration 3:   log pseudolikelihood = -10860.455  
Iteration 4:   log pseudolikelihood = -10860.421  
Iteration 5:   log pseudolikelihood = -10860.421  

Mixed-effects logistic regression               Number of obs     =     28,617
Group variable:     CountryYear                 Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(12)     =     621.74
Log pseudolikelihood = -10860.421               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               PolCon |   1.399506   .4313247     3.24   0.001     .5541251    2.244887
           Corruption |    .012459   .0118849     1.05   0.294     -.010835    .0357531
              GDPpcln |   .0019304   .1216519     0.02   0.987    -.2365029    .2403636
         Market_Ageln |  -.1960273   .1633348    -1.20   0.230    -.5161575    .1241029
            Workersln |   .3939423   .0198082    19.89   0.000     .3551189    .4327657
      EastAsiaPacific |  -.3139798   .4274983    -0.73   0.463    -1.151861    .5239015
    EuropeCentralAsia |  -.0490852    .384338    -0.13   0.898    -.8023739    .7042035
LatinAmericaCaribbean |   .2122158   .5140418     0.41   0.680    -.7952876    1.219719
            SouthAsia |   .8600458   .3359759     2.56   0.010     .2015451    1.518546
                      |
                 Year |
                2003  |  -.5057491   .2587399    -1.95   0.051     -1.01287    .0013716
                2004  |  -1.236734    .387817    -3.19   0.001    -1.996841   -.4766263
                2005  |  -.3925298   .1367118    -2.87   0.004      -.66048   -.1245797
                      |
                _cons |  -3.426849   1.647927    -2.08   0.038    -6.656725   -.1969718
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3583363   .0621232                      .2551067    .5033383
---------------------------------------------------------------------------------------

. melogit Lobby Polity     Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11444.201  
Iteration 1:   log likelihood = -11288.671  
Iteration 2:   log likelihood = -11287.957  
Iteration 3:   log likelihood = -11287.956  

Refining starting values:

Grid node 0:   log likelihood =  -10881.66

Fitting full model:

Iteration 0:   log pseudolikelihood =  -10881.66  (not concave)
Iteration 1:   log pseudolikelihood = -10867.681  
Iteration 2:   log pseudolikelihood = -10860.407  
Iteration 3:   log pseudolikelihood = -10858.669  
Iteration 4:   log pseudolikelihood = -10858.668  

Mixed-effects logistic regression               Number of obs     =     28,617
Group variable:     CountryYear                 Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(12)     =     586.08
Log pseudolikelihood = -10858.668               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               Polity |   .0606871   .0165057     3.68   0.000     .0283366    .0930377
           Corruption |   .0064458   .0112641     0.57   0.567    -.0156313     .028523
              GDPpcln |  -.1530886    .124869    -1.23   0.220    -.3978273    .0916502
         Market_Ageln |  -.1917348   .1595532    -1.20   0.229    -.5044534    .1209837
            Workersln |   .3938363   .0198096    19.88   0.000     .3550102    .4326623
      EastAsiaPacific |  -.1120531   .4583965    -0.24   0.807    -1.010494    .7863875
    EuropeCentralAsia |   .1510984   .4119876     0.37   0.714    -.6563825    .9585793
LatinAmericaCaribbean |   .1514252   .4631763     0.33   0.744    -.7563836    1.059234
            SouthAsia |   .8561201   .3298139     2.60   0.009     .2096968    1.502543
                      |
                 Year |
                2003  |   -.620403   .2650018    -2.34   0.019    -1.139797    -.101009
                2004  |  -1.299159   .3459122    -3.76   0.000    -1.977135   -.6211839
                2005  |  -.4926033   .1229735    -4.01   0.000    -.7336269   -.2515798
                      |
                _cons |  -2.315381   1.567766    -1.48   0.140    -5.388145    .7573833
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3436301   .0573969                      .2476931    .4767255
---------------------------------------------------------------------------------------

. melogit Lobby CGV        Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11496.041  
Iteration 1:   log likelihood = -11349.583  
Iteration 2:   log likelihood = -11348.947  
Iteration 3:   log likelihood = -11348.947  

Refining starting values:

Grid node 0:   log likelihood = -10884.008

Fitting full model:

Iteration 0:   log pseudolikelihood = -10884.008  (not concave)
Iteration 1:   log pseudolikelihood = -10871.365  
Iteration 2:   log pseudolikelihood = -10862.987  
Iteration 3:   log pseudolikelihood = -10862.779  
Iteration 4:   log pseudolikelihood = -10862.778  

Mixed-effects logistic regression               Number of obs     =     28,617
Group variable:     CountryYear                 Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(12)     =     528.24
Log pseudolikelihood = -10862.778               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  CGV |   .4830766   .2044562     2.36   0.018     .0823498    .8838034
           Corruption |   .0079681   .0110115     0.72   0.469     -.013614    .0295501
              GDPpcln |   -.031409   .1131279    -0.28   0.781    -.2531357    .1903176
         Market_Ageln |  -.1594586   .1617242    -0.99   0.324    -.4764323     .157515
            Workersln |    .393936   .0198431    19.85   0.000     .3550442    .4328278
      EastAsiaPacific |   -.373962   .5253192    -0.71   0.477    -1.403569    .6556448
    EuropeCentralAsia |  -.0559745   .4519542    -0.12   0.901    -.9417885    .8298395
LatinAmericaCaribbean |  -.1035149   .4468165    -0.23   0.817    -.9792593    .7722294
            SouthAsia |   .5907928   .3425613     1.72   0.085     -.080615    1.262201
                      |
                 Year |
                2003  |  -.5882509   .2948213    -2.00   0.046     -1.16609   -.0104117
                2004  |  -1.403908   .3663968    -3.83   0.000    -2.122033   -.6857837
                2005  |  -.5323382   .1351777    -3.94   0.000    -.7972815   -.2673948
                      |
                _cons |  -2.743092   1.502901    -1.83   0.068    -5.688723    .2025395
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3819965   .0669757                      .2709054    .5386431
---------------------------------------------------------------------------------------

. melogit Lobby BMR        Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -11483.348  
Iteration 1:   log likelihood = -11333.799  
Iteration 2:   log likelihood = -11333.137  
Iteration 3:   log likelihood = -11333.136  

Refining starting values:

Grid node 0:   log likelihood = -10884.763

Fitting full model:

Iteration 0:   log pseudolikelihood = -10884.763  (not concave)
Iteration 1:   log pseudolikelihood = -10871.708  
Iteration 2:   log pseudolikelihood = -10862.464  
Iteration 3:   log pseudolikelihood = -10862.038  
Iteration 4:   log pseudolikelihood = -10862.034  
Iteration 5:   log pseudolikelihood = -10862.034  

Mixed-effects logistic regression               Number of obs     =     28,617
Group variable:     CountryYear                 Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(12)     =     549.71
Log pseudolikelihood = -10862.034               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  BMR |    .573559   .2085388     2.75   0.006     .1648305    .9822875
           Corruption |   .0061946   .0107072     0.58   0.563    -.0147911    .0271803
              GDPpcln |  -.1051868   .1202591    -0.87   0.382    -.3408903    .1305166
         Market_Ageln |   -.162209    .154406    -1.05   0.293    -.4648393    .1404213
            Workersln |   .3937947   .0198255    19.86   0.000     .3549375     .432652
      EastAsiaPacific |  -.2817819   .5219155    -0.54   0.589    -1.304717    .7411537
    EuropeCentralAsia |   .0575324   .4434318     0.13   0.897     -.811578    .9266428
LatinAmericaCaribbean |  -.0019025   .4542698    -0.00   0.997     -.892255      .88845
            SouthAsia |   .6361255   .3484058     1.83   0.068    -.0467372    1.318988
                      |
                 Year |
                2003  |  -.6564606   .2986586    -2.20   0.028    -1.241821   -.0711005
                2004  |  -1.425886   .3814441    -3.74   0.000    -2.173503   -.6782693
                2005  |  -.5139629   .1360833    -3.78   0.000    -.7806814   -.2472445
                      |
                _cons |  -2.157739   1.491581    -1.45   0.148    -5.081185    .7657062
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3740876   .0644214                      .2669246    .5242738
---------------------------------------------------------------------------------------

. *Table 2: Note that since the "margin" command does not operate with melogit, we use xtmixed, the linear equiva
> lent of melogit, to obtain predicted probabilities
. xtmixed Lobby FHinverted Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
Note: single-variable random-effects specification in CountryYear equation; covariance structure set to identity

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log pseudolikelihood = -9903.2474  
Iteration 1:   log pseudolikelihood = -9903.2474  

Computing standard errors:

Mixed-effects regression                        Number of obs     =     28,617
Group variable: CountryYear                     Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

                                                Wald chi2(12)     =     367.70
Log pseudolikelihood = -9903.2474               Prob > chi2       =     0.0000

                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
           FHinverted |   .0146166   .0037883     3.86   0.000     .0071917    .0220415
           Corruption |   .0011979   .0012211     0.98   0.327    -.0011953    .0035912
              GDPpcln |  -.0248416    .013914    -1.79   0.074    -.0521125    .0024293
         Market_Ageln |  -.0229403    .024799    -0.93   0.355    -.0715453    .0256648
            Workersln |   .0513944   .0045864    11.21   0.000     .0424052    .0603835
      EastAsiaPacific |  -.0098061    .059883    -0.16   0.870    -.1271746    .1075624
    EuropeCentralAsia |   .0249206   .0579691     0.43   0.667    -.0886967    .1385379
LatinAmericaCaribbean |   .0083898   .0608366     0.14   0.890    -.1108476    .1276273
            SouthAsia |    .072445   .0452304     1.60   0.109     -.016205    .1610951
                      |
                 Year |
                2003  |  -.0986073   .0380888    -2.59   0.010    -.1732599   -.0239547
                2004  |  -.1572176   .0450544    -3.49   0.000    -.2455227   -.0689126
                2005  |  -.0739972   .0160701    -4.60   0.000    -.1054941   -.0425003
                      |
                _cons |   .1291498   .1690146     0.76   0.445    -.2021127    .4604123
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
                             |               Robust           
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
CountryYear: Identity        |
                   sd(_cons) |   .0785342   .0091166      .0625529    .0985984
-----------------------------+------------------------------------------------
                sd(Residual) |   .3406399    .010791      .3201332    .3624603
------------------------------------------------------------------------------

. margins, at (FHinverted=(0 12))

Predictive margins                              Number of obs     =     28,617
Model VCE    : Robust

Expression   : Linear prediction, fixed portion, predict()

1._at        : FHinverted      =           0

2._at        : FHinverted      =          12

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0543894   .0305979     1.78   0.075    -.0055813    .1143602
          2  |   .2297887   .0202229    11.36   0.000     .1901524    .2694249
------------------------------------------------------------------------------

. xtmixed Lobby PolCon     Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
Note: single-variable random-effects specification in CountryYear equation; covariance structure set to identity

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log pseudolikelihood = -9903.4224  
Iteration 1:   log pseudolikelihood = -9903.4224  

Computing standard errors:

Mixed-effects regression                        Number of obs     =     28,617
Group variable: CountryYear                     Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

                                                Wald chi2(12)     =     376.77
Log pseudolikelihood = -9903.4224               Prob > chi2       =     0.0000

                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               PolCon |   .1835584   .0513303     3.58   0.000     .0829529     .284164
           Corruption |   .0012216   .0013084     0.93   0.351    -.0013429     .003786
              GDPpcln |  -.0024927   .0153285    -0.16   0.871    -.0325359    .0275506
         Market_Ageln |   -.023882   .0248745    -0.96   0.337    -.0726351    .0248711
            Workersln |   .0513817   .0045854    11.21   0.000     .0423945    .0603689
      EastAsiaPacific |  -.0530021   .0503523    -1.05   0.293    -.1516907    .0456866
    EuropeCentralAsia |  -.0177074   .0509023    -0.35   0.728    -.1174742    .0820593
LatinAmericaCaribbean |   -.004751   .0705743    -0.07   0.946    -.1430741    .1335722
            SouthAsia |   .0735945   .0403819     1.82   0.068    -.0055525    .1527416
                      |
                 Year |
                2003  |  -.0766343   .0342856    -2.24   0.025    -.1438328   -.0094358
                2004  |  -.1492729   .0449479    -3.32   0.001    -.2373691   -.0611767
                2005  |  -.0566056   .0175366    -3.23   0.001    -.0909768   -.0222345
                      |
                _cons |   .0240811   .1906807     0.13   0.900    -.3496463    .3978084
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
                             |               Robust           
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
CountryYear: Identity        |
                   sd(_cons) |   .0788099   .0092147      .0626694    .0991072
-----------------------------+------------------------------------------------
                sd(Residual) |   .3406388   .0107914      .3201312    .3624601
------------------------------------------------------------------------------

. margins, at (PolCon=(0 0.75))

Predictive margins                              Number of obs     =     28,617
Model VCE    : Robust

Expression   : Linear prediction, fixed portion, predict()

1._at        : PolCon          =           0

2._at        : PolCon          =         .75

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1117637   .0188418     5.93   0.000     .0748344    .1486931
          2  |   .2494326   .0254236     9.81   0.000     .1996032    .2992619
------------------------------------------------------------------------------

. xtmixed Lobby Polity     Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
Note: single-variable random-effects specification in CountryYear equation; covariance structure set to identity

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log pseudolikelihood = -9902.1768  
Iteration 1:   log pseudolikelihood = -9902.1768  

Computing standard errors:

Mixed-effects regression                        Number of obs     =     28,617
Group variable: CountryYear                     Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

                                                Wald chi2(12)     =     299.28
Log pseudolikelihood = -9902.1768               Prob > chi2       =     0.0000

                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               Polity |   .0076579   .0019683     3.89   0.000     .0038001    .0115156
           Corruption |   .0004343   .0012261     0.35   0.723    -.0019687    .0028373
              GDPpcln |  -.0222103   .0161983    -1.37   0.170    -.0539584    .0095378
         Market_Ageln |  -.0229921   .0240169    -0.96   0.338    -.0700644    .0240802
            Workersln |   .0513704   .0045857    11.20   0.000     .0423827    .0603581
      EastAsiaPacific |   -.027342   .0546756    -0.50   0.617    -.1345042    .0798202
    EuropeCentralAsia |   .0093035   .0546119     0.17   0.865    -.0977339    .1163409
LatinAmericaCaribbean |  -.0118362   .0630089    -0.19   0.851    -.1353313    .1116589
            SouthAsia |    .071421    .041184     1.73   0.083    -.0092982    .1521402
                      |
                 Year |
                2003  |  -.0908799   .0355276    -2.56   0.011    -.1605126   -.0212471
                2004  |  -.1552409   .0402263    -3.86   0.000     -.234083   -.0763989
                2005  |  -.0697924   .0166517    -4.19   0.000    -.1024291   -.0371556
                      |
                _cons |   .1675728      .1824     0.92   0.358    -.1899246    .5250703
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
                             |               Robust           
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
CountryYear: Identity        |
                   sd(_cons) |   .0775215   .0087439       .062146    .0967011
-----------------------------+------------------------------------------------
                sd(Residual) |   .3406391   .0107918      .3201308    .3624612
------------------------------------------------------------------------------

. margins, at (Polity=(1 20))

Predictive margins                              Number of obs     =     28,617
Model VCE    : Robust

Expression   : Linear prediction, fixed portion, predict()

1._at        : Polity          =           1

2._at        : Polity          =          20

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0632213   .0274625     2.30   0.021     .0093959    .1170468
          2  |   .2087212   .0161354    12.94   0.000     .1770963     .240346
------------------------------------------------------------------------------

. xtmixed Lobby CGV        Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
Note: single-variable random-effects specification in CountryYear equation; covariance structure set to identity

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log pseudolikelihood = -9905.8626  
Iteration 1:   log pseudolikelihood = -9905.8626  

Computing standard errors:

Mixed-effects regression                        Number of obs     =     28,617
Group variable: CountryYear                     Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

                                                Wald chi2(12)     =     347.71
Log pseudolikelihood = -9905.8626               Prob > chi2       =     0.0000

                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  CGV |   .0627864   .0263736     2.38   0.017      .011095    .1144778
           Corruption |   .0006288   .0012016     0.52   0.601    -.0017263    .0029839
              GDPpcln |  -.0069999   .0138665    -0.50   0.614    -.0341778     .020178
         Market_Ageln |  -.0190566   .0242273    -0.79   0.432    -.0665411     .028428
            Workersln |   .0513802   .0045925    11.19   0.000      .042379    .0603813
      EastAsiaPacific |  -.0603974   .0631398    -0.96   0.339    -.1841492    .0633544
    EuropeCentralAsia |  -.0174388   .0587123    -0.30   0.766    -.1325128    .0976352
LatinAmericaCaribbean |  -.0444791   .0599398    -0.74   0.458    -.1619589    .0730007
            SouthAsia |   .0380596   .0435175     0.87   0.382    -.0472331    .1233522
                      |
                 Year |
                2003  |  -.0869973   .0388974    -2.24   0.025    -.1632347   -.0107598
                2004  |  -.1691567   .0436446    -3.88   0.000    -.2546984   -.0836149
                2005  |   -.074923   .0177918    -4.21   0.000    -.1097943   -.0400517
                      |
                _cons |   .1143611   .1700937     0.67   0.501    -.2190165    .4477386
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
                             |               Robust           
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
CountryYear: Identity        |
                   sd(_cons) |   .0813335   .0089851       .065499     .100996
-----------------------------+------------------------------------------------
                sd(Residual) |   .3406388   .0107916       .320131    .3624603
------------------------------------------------------------------------------

. margins, at (CGV=(0 1))

Predictive margins                              Number of obs     =     28,617
Model VCE    : Robust

Expression   : Linear prediction, fixed portion, predict()

1._at        : CGV             =           0

2._at        : CGV             =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1278666   .0208871     6.12   0.000     .0869286    .1688047
          2  |    .190653   .0145508    13.10   0.000     .1621339    .2191721
------------------------------------------------------------------------------

. xtmixed Lobby BMR        Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific EuropeCentralAsia LatinAmeri
> caCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
Note: single-variable random-effects specification in CountryYear equation; covariance structure set to identity

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log pseudolikelihood = -9904.9622  
Iteration 1:   log pseudolikelihood = -9904.9622  

Computing standard errors:

Mixed-effects regression                        Number of obs     =     28,617
Group variable: CountryYear                     Number of groups  =         83

                                                Obs per group:
                                                              min =        100
                                                              avg =      344.8
                                                              max =      1,489

                                                Wald chi2(12)     =     330.87
Log pseudolikelihood = -9904.9622               Prob > chi2       =     0.0000

                                        (Std. Err. adjusted for 50 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  BMR |   .0760525   .0287916     2.64   0.008     .0196219     .132483
           Corruption |   .0004086   .0011577     0.35   0.724    -.0018604    .0026777
              GDPpcln |  -.0167493   .0154029    -1.09   0.277    -.0469384    .0134397
         Market_Ageln |  -.0195541   .0232114    -0.84   0.400    -.0650477    .0259395
            Workersln |   .0513648   .0045917    11.19   0.000     .0423651    .0603644
      EastAsiaPacific |  -.0479757   .0634373    -0.76   0.449    -.1723106    .0763592
    EuropeCentralAsia |  -.0026407   .0588755    -0.04   0.964    -.1180345    .1127532
LatinAmericaCaribbean |  -.0317198   .0619667    -0.51   0.609    -.1531723    .0897327
            SouthAsia |   .0446665   .0446433     1.00   0.317    -.0428328    .1321658
                      |
                 Year |
                2003  |  -.0960019   .0396998    -2.42   0.016     -.173812   -.0181918
                2004  |  -.1730059   .0458932    -3.77   0.000     -.262955   -.0830569
                2005  |  -.0725288   .0180716    -4.01   0.000    -.1079484   -.0371092
                      |
                _cons |   .1904512   .1690075     1.13   0.260    -.1407974    .5216997
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
                             |               Robust           
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
CountryYear: Identity        |
                   sd(_cons) |   .0803762   .0086382      .0651098     .099222
-----------------------------+------------------------------------------------
                sd(Residual) |    .340639    .010792      .3201304    .3624614
------------------------------------------------------------------------------

. margins, at (BMR=(0 1))

Predictive margins                              Number of obs     =     28,617
Model VCE    : Robust

Expression   : Linear prediction, fixed portion, predict()

1._at        : BMR             =           0

2._at        : BMR             =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1184595   .0218161     5.43   0.000     .0757008    .1612182
          2  |    .194512     .01512    12.86   0.000     .1648774    .2241466
------------------------------------------------------------------------------

. *Table 3
. melogit Lobby FHinverted Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnership
>  GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction S
> ervices EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(c
> luster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -9108.8619  
Iteration 1:   log likelihood = -8938.6554  
Iteration 2:   log likelihood = -8937.6469  
Iteration 3:   log likelihood = -8937.6466  

Refining starting values:

Grid node 0:   log likelihood = -8671.6586

Fitting full model:

Iteration 0:   log pseudolikelihood = -8671.6586  (not concave)
Iteration 1:   log pseudolikelihood = -8659.9568  
Iteration 2:   log pseudolikelihood = -8651.7274  
Iteration 3:   log pseudolikelihood = -8640.0936  
Iteration 4:   log pseudolikelihood = -8640.0735  
Iteration 5:   log pseudolikelihood = -8640.0735  

Mixed-effects logistic regression               Number of obs     =     24,107
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.2
                                                              max =      1,368

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1576.99
Log pseudolikelihood = -8640.0735               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
           FHinverted |   .1097722   .0317504     3.46   0.001     .0475426    .1720018
           Corruption |   .0106736   .0110515     0.97   0.334    -.0109868    .0323341
              GDPpcln |  -.2029057   .1052677    -1.93   0.054    -.4092267    .0034153
         Market_Ageln |  -.1513768   .1616735    -0.94   0.349    -.4682509    .1654974
            Workersln |   .3316116   .0192889    17.19   0.000     .2938061    .3694171
           Firm_Ageln |    .179941   .0308178     5.84   0.000     .1195392    .2403427
     LocatedinCapital |   .1423612   .0733577     1.94   0.052    -.0014172    .2861396
     ForeignOwnership |   .0017385   .0009683     1.80   0.073    -.0001595    .0036364
        GovtOwnership |   .0036452   .0010264     3.55   0.000     .0016334    .0056569
          SalestoGovt |   .0020953   .0014645     1.43   0.153    -.0007751    .0049657
       PubliclyListed |  -.0081018   .0839337    -0.10   0.923    -.1726088    .1564051
       DomesticInputs |  -.0028734   .0006805    -4.22   0.000    -.0042073   -.0015396
            Exporting |   .2389787   .0584205     4.09   0.000     .1244766    .3534807
                  MNC |   .1068927   .0930682     1.15   0.251    -.0755176     .289303
        Manufacturing |  -.8941696   .1834255    -4.87   0.000    -1.253677   -.5346622
          Agriculture |  -.4583522   .2222514    -2.06   0.039    -.8939569   -.0227476
         Construction |  -.7581845   .1959998    -3.87   0.000    -1.142337    -.374032
             Services |  -.4736922   .1670333    -2.84   0.005    -.8010713    -.146313
      EastAsiaPacific |   .2214072   .5263214     0.42   0.674    -.8101639    1.252978
    EuropeCentralAsia |   .2960629    .507554     0.58   0.560    -.6987246     1.29085
LatinAmericaCaribbean |   .6081588    .509724     1.19   0.233    -.3908819    1.607199
            SouthAsia |   1.229169   .5285632     2.33   0.020     .1932046    2.265134
                      |
                 Year |
                2003  |  -.7020349   .3298184    -2.13   0.033    -1.348467   -.0556028
                2004  |  -.8912958   .2765557    -3.22   0.001    -1.433335   -.3492566
                2005  |  -.4600313   .1140028    -4.04   0.000    -.6834727   -.2365899
                      |
                _cons |  -2.007557    1.52171    -1.32   0.187    -4.990054    .9749404
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3227307   .0661069                      .2160148    .4821665
---------------------------------------------------------------------------------------

. melogit Lobby PolCon     Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnership
>  GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction S
> ervices EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(c
> luster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -9116.5774  
Iteration 1:   log likelihood =  -8951.588  
Iteration 2:   log likelihood = -8950.6712  
Iteration 3:   log likelihood =  -8950.671  

Refining starting values:

Grid node 0:   log likelihood = -8671.8746

Fitting full model:

Iteration 0:   log pseudolikelihood = -8671.8746  (not concave)
Iteration 1:   log pseudolikelihood = -8660.1204  
Iteration 2:   log pseudolikelihood = -8651.5847  
Iteration 3:   log pseudolikelihood = -8639.8919  
Iteration 4:   log pseudolikelihood = -8639.8861  
Iteration 5:   log pseudolikelihood = -8639.8861  

Mixed-effects logistic regression               Number of obs     =     24,107
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.2
                                                              max =      1,368

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1320.32
Log pseudolikelihood = -8639.8861               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               PolCon |   1.488913   .4051377     3.68   0.000     .6948573    2.282968
           Corruption |   .0103799   .0113394     0.92   0.360    -.0118448    .0326047
              GDPpcln |  -.0494117   .1160998    -0.43   0.670    -.2769631    .1781397
         Market_Ageln |  -.1436598   .1611072    -0.89   0.373    -.4594242    .1721045
            Workersln |   .3311991   .0193022    17.16   0.000     .2933676    .3690307
           Firm_Ageln |   .1806652   .0308369     5.86   0.000      .120226    .2411045
     LocatedinCapital |   .1420237   .0730627     1.94   0.052    -.0011767     .285224
     ForeignOwnership |   .0017546   .0009697     1.81   0.070     -.000146    .0036552
        GovtOwnership |   .0036629   .0010261     3.57   0.000     .0016517     .005674
          SalestoGovt |   .0020733   .0014567     1.42   0.155    -.0007818    .0049284
       PubliclyListed |  -.0066918   .0842804    -0.08   0.937    -.1718784    .1584948
       DomesticInputs |  -.0028931   .0006818    -4.24   0.000    -.0042294   -.0015569
            Exporting |   .2395252   .0584623     4.10   0.000     .1249412    .3541092
                  MNC |   .1069689   .0932739     1.15   0.251    -.0758446    .2897825
        Manufacturing |  -.8877826   .1832823    -4.84   0.000    -1.247009   -.5285559
          Agriculture |  -.4512675   .2234802    -2.02   0.043    -.8892807   -.0132544
         Construction |  -.7526915   .1958952    -3.84   0.000    -1.136639    -.368744
             Services |  -.4681832   .1672914    -2.80   0.005    -.7960683   -.1402981
      EastAsiaPacific |  -.0044865    .447813    -0.01   0.992    -.8821839    .8732108
    EuropeCentralAsia |   .0823346   .4452484     0.18   0.853    -.7903363    .9550055
LatinAmericaCaribbean |    .613941   .5585182     1.10   0.272    -.4807346    1.708617
            SouthAsia |   1.356014   .4713111     2.88   0.004     .4322612    2.279767
                      |
                 Year |
                2003  |   -.491564   .2855068    -1.72   0.085    -1.051147     .068019
                2004  |  -.8189972   .2956869    -2.77   0.006    -1.398533   -.2394614
                2005  |  -.3171055   .1420381    -2.23   0.026    -.5954951    -.038716
                      |
                _cons |  -2.865438   1.651476    -1.74   0.083    -6.102272    .3713961
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3208827   .0655141                      .2150592    .4787786
---------------------------------------------------------------------------------------

. melogit Lobby Polity     Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnership
>  GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction S
> ervices EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(c
> luster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -9113.2645  
Iteration 1:   log likelihood = -8943.6013  
Iteration 2:   log likelihood = -8942.6228  
Iteration 3:   log likelihood = -8942.6226  

Refining starting values:

Grid node 0:   log likelihood = -8671.2137

Fitting full model:

Iteration 0:   log pseudolikelihood = -8671.2137  (not concave)
Iteration 1:   log pseudolikelihood = -8659.4703  
Iteration 2:   log pseudolikelihood = -8650.8526  
Iteration 3:   log pseudolikelihood = -8639.5219  
Iteration 4:   log pseudolikelihood = -8639.5161  
Iteration 5:   log pseudolikelihood = -8639.5161  

Mixed-effects logistic regression               Number of obs     =     24,107
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.2
                                                              max =      1,368

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1654.25
Log pseudolikelihood = -8639.5161               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               Polity |   .0560139   .0147706     3.79   0.000     .0270641    .0849638
           Corruption |   .0054308   .0111965     0.49   0.628    -.0165139    .0273754
              GDPpcln |  -.1681078   .1215876    -1.38   0.167    -.4064152    .0701996
         Market_Ageln |  -.1506945   .1556086    -0.97   0.333    -.4556818    .1542927
            Workersln |   .3314707    .019287    17.19   0.000     .2936689    .3692724
           Firm_Ageln |   .1797224   .0307625     5.84   0.000      .119429    .2400159
     LocatedinCapital |   .1420197   .0733775     1.94   0.053    -.0017975     .285837
     ForeignOwnership |   .0017536   .0009705     1.81   0.071    -.0001484    .0036557
        GovtOwnership |   .0036609   .0010265     3.57   0.000     .0016489    .0056729
          SalestoGovt |   .0021232   .0014622     1.45   0.147    -.0007428    .0049891
       PubliclyListed |  -.0052139   .0837808    -0.06   0.950    -.1694213    .1589934
       DomesticInputs |   -.002879   .0006818    -4.22   0.000    -.0042153   -.0015427
            Exporting |   .2391312   .0583998     4.09   0.000     .1246697    .3535927
                  MNC |   .1049299   .0932836     1.12   0.261    -.0779025    .2877624
        Manufacturing |  -.8925961   .1834913    -4.86   0.000    -1.252232   -.5329598
          Agriculture |  -.4602074   .2226324    -2.07   0.039    -.8965587    -.023856
         Construction |  -.7566668   .1963262    -3.85   0.000    -1.141459   -.3718745
             Services |  -.4722322   .1673801    -2.82   0.005    -.8002913   -.1441732
      EastAsiaPacific |   .1163431    .487285     0.24   0.811     -.838718    1.071404
    EuropeCentralAsia |   .2101943   .4778446     0.44   0.660    -.7263639    1.146752
LatinAmericaCaribbean |   .4801023   .5124588     0.94   0.349    -.5242986    1.484503
            SouthAsia |   1.299562   .4944373     2.63   0.009     .3304827    2.268641
                      |
                 Year |
                2003  |  -.6319973   .3077948    -2.05   0.040    -1.235264   -.0287306
                2004  |  -.9692048    .287786    -3.37   0.001    -1.533255   -.4051547
                2005  |  -.4348189    .120003    -3.62   0.000    -.6700204   -.1996174
                      |
                _cons |  -1.885614   1.627546    -1.16   0.247    -5.075546    1.304318
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3186337   .0647797                      .2139137    .4746187
---------------------------------------------------------------------------------------

. melogit Lobby CGV        Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnership
>  GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction S
> ervices EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(c
> luster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -9159.1481  
Iteration 1:   log likelihood = -9000.0358  
Iteration 2:   log likelihood = -8999.1798  
Iteration 3:   log likelihood = -8999.1796  

Refining starting values:

Grid node 0:   log likelihood = -8676.5084

Fitting full model:

Iteration 0:   log pseudolikelihood = -8676.5084  (not concave)
Iteration 1:   log pseudolikelihood = -8665.2987  
Iteration 2:   log pseudolikelihood = -8650.2974  
Iteration 3:   log pseudolikelihood =  -8644.321  
Iteration 4:   log pseudolikelihood = -8644.0867  
Iteration 5:   log pseudolikelihood = -8644.0851  
Iteration 6:   log pseudolikelihood = -8644.0851  

Mixed-effects logistic regression               Number of obs     =     24,107
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.2
                                                              max =      1,368

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1624.17
Log pseudolikelihood = -8644.0851               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  CGV |   .3661539   .2175321     1.68   0.092    -.0602011     .792509
           Corruption |   .0066175   .0111701     0.59   0.554    -.0152756    .0285105
              GDPpcln |  -.0474868   .1155656    -0.41   0.681    -.2739911    .1790176
         Market_Ageln |  -.1122767   .1604749    -0.70   0.484    -.4268018    .2022483
            Workersln |   .3312206   .0193108    17.15   0.000     .2933722     .369069
           Firm_Ageln |   .1808427   .0307592     5.88   0.000     .1205558    .2411296
     LocatedinCapital |   .1429971   .0733957     1.95   0.051    -.0008558    .2868501
     ForeignOwnership |   .0017415   .0009684     1.80   0.072    -.0001566    .0036396
        GovtOwnership |   .0036664   .0010258     3.57   0.000      .001656    .0056768
          SalestoGovt |   .0020141   .0014655     1.37   0.169    -.0008582    .0048864
       PubliclyListed |  -.0059368   .0842006    -0.07   0.944    -.1709668    .1590933
       DomesticInputs |  -.0028764   .0006843    -4.20   0.000    -.0042175   -.0015353
            Exporting |   .2401515    .058455     4.11   0.000     .1255819    .3547211
                  MNC |   .1070573   .0930715     1.15   0.250    -.0753595     .289474
        Manufacturing |   -.894494   .1833452    -4.88   0.000    -1.253844   -.5351439
          Agriculture |  -.4589744   .2210112    -2.08   0.038    -.8921484   -.0258005
         Construction |  -.7580101   .1963015    -3.86   0.000    -1.142754   -.3732663
             Services |  -.4721948   .1671532    -2.82   0.005    -.7998091   -.1445806
      EastAsiaPacific |  -.1948711   .5891907    -0.33   0.741    -1.349664    .9599215
    EuropeCentralAsia |  -.0191425   .5519052    -0.03   0.972    -1.100857    1.062572
LatinAmericaCaribbean |   .2169946   .5497276     0.39   0.693    -.8604517    1.294441
            SouthAsia |   1.007461   .5229062     1.93   0.054    -.0174159    2.032339
                      |
                 Year |
                2003  |  -.5926317   .3275841    -1.81   0.070    -1.234685    .0494213
                2004  |  -1.040682   .2742172    -3.80   0.000    -1.578138   -.5032257
                2005  |  -.4671365   .1317461    -3.55   0.000    -.7253542   -.2089189
                      |
                _cons |    -2.2676   1.602474    -1.42   0.157    -5.408391    .8731912
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3634143    .076187                       .240965    .5480875
---------------------------------------------------------------------------------------

. melogit Lobby BMR        Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnership
>  GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction S
> ervices EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(c
> luster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -9147.5383  
Iteration 1:   log likelihood = -8985.0806  
Iteration 2:   log likelihood = -8984.1772  
Iteration 3:   log likelihood =  -8984.177  

Refining starting values:

Grid node 0:   log likelihood =  -8676.715

Fitting full model:

Iteration 0:   log pseudolikelihood =  -8676.715  (not concave)
Iteration 1:   log pseudolikelihood =  -8665.355  
Iteration 2:   log pseudolikelihood = -8653.9365  
Iteration 3:   log pseudolikelihood = -8643.3211  
Iteration 4:   log pseudolikelihood = -8643.3141  
Iteration 5:   log pseudolikelihood = -8643.3141  

Mixed-effects logistic regression               Number of obs     =     24,107
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.2
                                                              max =      1,368

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1510.53
Log pseudolikelihood = -8643.3141               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
                Lobby |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  BMR |   .4702895   .2109572     2.23   0.026      .056821    .8837579
           Corruption |   .0052586   .0108323     0.49   0.627    -.0159724    .0264896
              GDPpcln |  -.1120157    .117207    -0.96   0.339    -.3417371    .1177058
         Market_Ageln |  -.1211004   .1522943    -0.80   0.427    -.4195917    .1773908
            Workersln |   .3310577   .0193004    17.15   0.000     .2932296    .3688858
           Firm_Ageln |   .1809911   .0306875     5.90   0.000     .1208446    .2411375
     LocatedinCapital |   .1437747   .0734178     1.96   0.050    -.0001216     .287671
     ForeignOwnership |   .0017438   .0009676     1.80   0.072    -.0001526    .0036402
        GovtOwnership |   .0036594   .0010257     3.57   0.000     .0016491    .0056698
          SalestoGovt |   .0020202   .0014678     1.38   0.169    -.0008566     .004897
       PubliclyListed |  -.0060858   .0842568    -0.07   0.942    -.1712261    .1590545
       DomesticInputs |  -.0028796   .0006842    -4.21   0.000    -.0042206   -.0015386
            Exporting |   .2393943   .0584506     4.10   0.000     .1248333    .3539554
                  MNC |   .1070342   .0931291     1.15   0.250    -.0754956     .289564
        Manufacturing |  -.8953478   .1834919    -4.88   0.000    -1.254985   -.5357102
          Agriculture |  -.4520075   .2225336    -2.03   0.042    -.8881654   -.0158496
         Construction |  -.7590965   .1960378    -3.87   0.000    -1.143324   -.3748695
             Services |  -.4741339   .1668731    -2.84   0.004    -.8011991   -.1470686
      EastAsiaPacific |  -.0890352   .5728172    -0.16   0.876    -1.211736    1.033666
    EuropeCentralAsia |   .0925215   .5343325     0.17   0.863    -.9547509    1.139794
LatinAmericaCaribbean |   .3152776   .5339323     0.59   0.555    -.7312104    1.361766
            SouthAsia |   1.058726   .5323503     1.99   0.047     .0153387    2.102113
                      |
                 Year |
                2003  |  -.6567519   .3417837    -1.92   0.055    -1.326636    .0131319
                2004  |   -1.05205   .2822991    -3.73   0.000    -1.605346   -.4987542
                2005  |   -.455377   .1311735    -3.47   0.001    -.7124723   -.1982817
                      |
                _cons |  -1.774551   1.585602    -1.12   0.263    -4.882274    1.333172
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3547716   .0729725                      .2370643    .5309229
---------------------------------------------------------------------------------------

. *Table 4
. melogit LobbyBA FHinverted Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnersh
> ip GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction
>  Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce
> (cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -12906.469  
Iteration 1:   log likelihood = -12893.931  
Iteration 2:   log likelihood = -12893.921  
Iteration 3:   log likelihood = -12893.921  

Refining starting values:

Grid node 0:   log likelihood = -12447.548

Fitting full model:

Iteration 0:   log pseudolikelihood = -12447.548  (not concave)
Iteration 1:   log pseudolikelihood = -12431.861  
Iteration 2:   log pseudolikelihood = -12403.081  
Iteration 3:   log pseudolikelihood = -12394.242  
Iteration 4:   log pseudolikelihood = -12394.204  
Iteration 5:   log pseudolikelihood = -12394.203  

Mixed-effects logistic regression               Number of obs     =     24,150
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.8
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    2252.06
Log pseudolikelihood = -12394.203               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
              LobbyBA |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
           FHinverted |   .0872506   .0312807     2.79   0.005     .0259416    .1485596
           Corruption |  -.0122893   .0094484    -1.30   0.193    -.0308079    .0062292
              GDPpcln |  -.1453491   .1025278    -1.42   0.156    -.3462998    .0556016
         Market_Ageln |    .070048   .1780767     0.39   0.694    -.2789758    .4190719
            Workersln |   .3548857    .020874    17.00   0.000     .3139735    .3957979
           Firm_Ageln |   .2083026   .0245606     8.48   0.000     .1601647    .2564406
     LocatedinCapital |    .123482   .0683631     1.81   0.071    -.0105073    .2574712
     ForeignOwnership |   .0013056   .0009215     1.42   0.157    -.0005006    .0031118
        GovtOwnership |   .0005638   .0012045     0.47   0.640    -.0017971    .0029247
          SalestoGovt |   .0014601   .0012642     1.15   0.248    -.0010177    .0039379
       PubliclyListed |   .0600553   .1137056     0.53   0.597    -.1628035    .2829142
       DomesticInputs |  -.0031063   .0006946    -4.47   0.000    -.0044678   -.0017449
            Exporting |   .2875874   .0503386     5.71   0.000     .1889256    .3862493
                  MNC |   .1846207   .0815749     2.26   0.024     .0247369    .3445045
        Manufacturing |  -.5354306   .1730711    -3.09   0.002    -.8746436   -.1962175
          Agriculture |  -.1536299   .2114217    -0.73   0.467    -.5680088    .2607491
         Construction |  -.4124624   .1999466    -2.06   0.039    -.8043505   -.0205742
             Services |  -.3133286   .1659561    -1.89   0.059    -.6385967    .0119395
      EastAsiaPacific |  -1.103551   .4271474    -2.58   0.010    -1.940744   -.2663575
    EuropeCentralAsia |  -.6979054   .4049147    -1.72   0.085    -1.491524    .0957129
LatinAmericaCaribbean |  -.4615609   .9403061    -0.49   0.624    -2.304527    1.381405
            SouthAsia |   .2130604   .7735558     0.28   0.783    -1.303081    1.729202
                      |
                 Year |
                2003  |  -.3547317   .3057368    -1.16   0.246    -.9539649    .2445016
                2004  |   .0927673   .5798197     0.16   0.873    -1.043658    1.229193
                2005  |  -.1509788   .1147597    -1.32   0.188    -.3759037    .0739461
                      |
                _cons |  -.2684484   1.389103    -0.19   0.847     -2.99104    2.454143
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3175811   .0732198                      .2021185     .499003
---------------------------------------------------------------------------------------

. melogit LobbyBA PolCon     Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnersh
> ip GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction
>  Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce
> (cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -12899.08  
Iteration 1:   log likelihood = -12888.658  
Iteration 2:   log likelihood = -12888.651  
Iteration 3:   log likelihood = -12888.651  

Refining starting values:

Grid node 0:   log likelihood = -12447.044

Fitting full model:

Iteration 0:   log pseudolikelihood = -12447.044  (not concave)
Iteration 1:   log pseudolikelihood = -12431.709  
Iteration 2:   log pseudolikelihood = -12403.263  
Iteration 3:   log pseudolikelihood = -12394.507  
Iteration 4:   log pseudolikelihood = -12394.472  
Iteration 5:   log pseudolikelihood = -12394.472  

Mixed-effects logistic regression               Number of obs     =     24,150
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.8
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    2356.02
Log pseudolikelihood = -12394.472               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
              LobbyBA |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               PolCon |   1.130532   .4254549     2.66   0.008     .2966554    1.964408
           Corruption |  -.0127689   .0089768    -1.42   0.155    -.0303631    .0048253
              GDPpcln |  -.0220167   .1146888    -0.19   0.848    -.2468025    .2027692
         Market_Ageln |   .0791527   .1724298     0.46   0.646    -.2588034    .4171089
            Workersln |    .354648   .0208762    16.99   0.000     .3137314    .3955646
           Firm_Ageln |   .2087081   .0245881     8.49   0.000     .1605163    .2568999
     LocatedinCapital |   .1233973   .0684122     1.80   0.071    -.0106881    .2574827
     ForeignOwnership |    .001312   .0009228     1.42   0.155    -.0004968    .0031207
        GovtOwnership |   .0005758   .0012043     0.48   0.633    -.0017846    .0029362
          SalestoGovt |   .0014423   .0012595     1.15   0.252    -.0010262    .0039108
       PubliclyListed |   .0610323   .1138245     0.54   0.592    -.1620595    .2841242
       DomesticInputs |  -.0031164   .0006964    -4.48   0.000    -.0044813   -.0017515
            Exporting |   .2880259   .0503273     5.72   0.000     .1893861    .3866657
                  MNC |   .1847318   .0817171     2.26   0.024     .0245693    .3448942
        Manufacturing |  -.5322969    .173061    -3.08   0.002    -.8714903   -.1931035
          Agriculture |  -.1503669   .2105234    -0.71   0.475    -.5629851    .2622513
         Construction |  -.4098726   .1998411    -2.05   0.040     -.801554   -.0181913
             Services |  -.3107098   .1660686    -1.87   0.061    -.6361983    .0147787
      EastAsiaPacific |  -1.296768   .4481604    -2.89   0.004    -2.175147     -.41839
    EuropeCentralAsia |  -.8759733   .3937507    -2.22   0.026     -1.64771   -.1042362
LatinAmericaCaribbean |  -.4736635   1.039698    -0.46   0.649    -2.511434    1.564107
            SouthAsia |    .294414   .7688866     0.38   0.702    -1.212576    1.801404
                      |
                 Year |
                2003  |  -.1891965   .2961862    -0.64   0.523    -.7697107    .3913178
                2004  |   .1462433   .5960214     0.25   0.806    -1.021937    1.314424
                2005  |  -.0434084   .1340171    -0.32   0.746    -.3060771    .2192602
                      |
                _cons |  -.9232202   1.454486    -0.63   0.526     -3.77396    1.927519
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|    .319313   .0766837                      .1994338    .5112512
---------------------------------------------------------------------------------------

. melogit LobbyBA Polity     Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnersh
> ip GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction
>  Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce
> (cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -12881.195  
Iteration 1:   log likelihood = -12865.876  
Iteration 2:   log likelihood = -12865.854  
Iteration 3:   log likelihood = -12865.854  

Refining starting values:

Grid node 0:   log likelihood = -12444.863

Fitting full model:

Iteration 0:   log pseudolikelihood = -12444.863  (not concave)
Iteration 1:   log pseudolikelihood = -12428.853  
Iteration 2:   log pseudolikelihood = -12401.711  
Iteration 3:   log pseudolikelihood = -12393.275  
Iteration 4:   log pseudolikelihood = -12393.189  
Iteration 5:   log pseudolikelihood = -12393.189  

Mixed-effects logistic regression               Number of obs     =     24,150
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.8
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    2289.33
Log pseudolikelihood = -12393.189               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
              LobbyBA |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               Polity |   .0475726   .0154259     3.08   0.002     .0173383    .0778069
           Corruption |  -.0164013   .0086077    -1.91   0.057    -.0332722    .0004696
              GDPpcln |  -.1271355   .1188298    -1.07   0.285    -.3600377    .1057667
         Market_Ageln |   .0651595   .1681495     0.39   0.698    -.2644075    .3947264
            Workersln |   .3547897   .0208682    17.00   0.000     .3138887    .3956906
           Firm_Ageln |   .2081273   .0245581     8.47   0.000     .1599943    .2562604
     LocatedinCapital |   .1231188   .0685367     1.80   0.072    -.0112106    .2574482
     ForeignOwnership |   .0013147   .0009225     1.43   0.154    -.0004933    .0031227
        GovtOwnership |   .0005686   .0012063     0.47   0.637    -.0017956    .0029329
          SalestoGovt |   .0014884   .0012649     1.18   0.239    -.0009908    .0039677
       PubliclyListed |   .0620056    .113672     0.55   0.585    -.1607875    .2847987
       DomesticInputs |  -.0031078   .0006965    -4.46   0.000    -.0044729   -.0017427
            Exporting |   .2876025   .0503385     5.71   0.000     .1889409    .3862641
                  MNC |   .1835251   .0816578     2.25   0.025     .0234787    .3435715
        Manufacturing |  -.5349603   .1731407    -3.09   0.002    -.8743098   -.1956108
          Agriculture |  -.1551232   .2112485    -0.73   0.463    -.5691627    .2589163
         Construction |  -.4119866   .2000348    -2.06   0.039    -.8040475   -.0199256
             Services |  -.3128835   .1661723    -1.88   0.060    -.6385752    .0128082
      EastAsiaPacific |  -1.170724     .43384    -2.70   0.007    -2.021035   -.3204133
    EuropeCentralAsia |  -.7618569   .4007995    -1.90   0.057    -1.547409    .0236958
LatinAmericaCaribbean |  -.5579394   .9670314    -0.58   0.564    -2.453286    1.337407
            SouthAsia |   .2756856   .7829024     0.35   0.725    -1.258775    1.810146
                      |
                 Year |
                2003  |  -.3024598   .2985513    -1.01   0.311    -.8876096      .28269
                2004  |    .032875   .5952325     0.06   0.956    -1.133759    1.199509
                2005  |    -.12879   .1157006    -1.11   0.266    -.3555591    .0979791
                      |
                _cons |  -.1295042   1.404501    -0.09   0.927    -2.882276    2.623267
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3084204   .0698925                      .1978101    .4808812
---------------------------------------------------------------------------------------

. melogit LobbyBA CGV        Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnersh
> ip GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction
>  Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce
> (cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -12898.021  
Iteration 1:   log likelihood = -12883.592  
Iteration 2:   log likelihood = -12883.573  
Iteration 3:   log likelihood = -12883.573  

Refining starting values:

Grid node 0:   log likelihood = -12444.956

Fitting full model:

Iteration 0:   log pseudolikelihood = -12444.956  (not concave)
Iteration 1:   log pseudolikelihood = -12429.656  
Iteration 2:   log pseudolikelihood = -12403.024  
Iteration 3:   log pseudolikelihood = -12394.832  
Iteration 4:   log pseudolikelihood = -12394.808  
Iteration 5:   log pseudolikelihood = -12394.808  

Mixed-effects logistic regression               Number of obs     =     24,150
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.8
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    2065.23
Log pseudolikelihood = -12394.808               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
              LobbyBA |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  CGV |   .4730306   .1910707     2.48   0.013      .098539    .8475222
           Corruption |  -.0144965   .0085728    -1.69   0.091    -.0312988    .0023058
              GDPpcln |  -.0423934   .1026322    -0.41   0.680    -.2435488     .158762
         Market_Ageln |   .0748115   .1665325     0.45   0.653    -.2515862    .4012091
            Workersln |   .3548824   .0208818    16.99   0.000     .3139547      .39581
           Firm_Ageln |   .2083973   .0245749     8.48   0.000     .1602314    .2565631
     LocatedinCapital |   .1229345   .0686594     1.79   0.073    -.0116354    .2575044
     ForeignOwnership |   .0013069   .0009216     1.42   0.156    -.0004995    .0031132
        GovtOwnership |   .0005699   .0012041     0.47   0.636    -.0017901    .0029298
          SalestoGovt |   .0014444   .0012656     1.14   0.254     -.001036    .0039249
       PubliclyListed |   .0599794   .1137987     0.53   0.598    -.1630619    .2830207
       DomesticInputs |  -.0031013   .0006965    -4.45   0.000    -.0044665   -.0017361
            Exporting |   .2881784   .0502587     5.73   0.000     .1896732    .3866835
                  MNC |   .1845237   .0815701     2.26   0.024     .0246493    .3443982
        Manufacturing |  -.5359887   .1729828    -3.10   0.002    -.8750287   -.1969487
          Agriculture |  -.1583211    .211511    -0.75   0.454     -.572875    .2562327
         Construction |  -.4128296   .2000873    -2.06   0.039    -.8049934   -.0206658
             Services |  -.3128865   .1659105    -1.89   0.059    -.6380652    .0122922
      EastAsiaPacific |  -1.438011   .5021403    -2.86   0.004    -2.422188   -.4538342
    EuropeCentralAsia |  -1.019103   .4160481    -2.45   0.014    -1.834542   -.2036635
LatinAmericaCaribbean |   -.864983   .9081394    -0.95   0.341    -2.644904    .9149376
            SouthAsia |   .0205425   .7635328     0.03   0.979    -1.475954    1.517039
                      |
                 Year |
                2003  |  -.2915696   .3160043    -0.92   0.356    -.9109266    .3277874
                2004  |  -.0825559   .5927577    -0.14   0.889     -1.24434    1.079228
                2005  |  -.1550093   .1214751    -1.28   0.202    -.3930961    .0830775
                      |
                _cons |  -.3450949   1.331341    -0.26   0.795    -2.954476    2.264286
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3226467   .0756862                       .203729    .5109771
---------------------------------------------------------------------------------------

. melogit LobbyBA BMR        Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwnersh
> ip GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Construction
>  Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce
> (cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -12925.947  
Iteration 1:   log likelihood = -12913.714  
Iteration 2:   log likelihood = -12913.703  
Iteration 3:   log likelihood = -12913.703  

Refining starting values:

Grid node 0:   log likelihood = -12453.625

Fitting full model:

Iteration 0:   log pseudolikelihood = -12453.625  (not concave)
Iteration 1:   log pseudolikelihood = -12438.279  
Iteration 2:   log pseudolikelihood =  -12405.94  
Iteration 3:   log pseudolikelihood = -12395.712  
Iteration 4:   log pseudolikelihood = -12395.687  
Iteration 5:   log pseudolikelihood = -12395.687  

Mixed-effects logistic regression               Number of obs     =     24,150
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.8
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    2401.38
Log pseudolikelihood = -12395.687               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
              LobbyBA |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  BMR |   .4404514   .2052622     2.15   0.032     .0381449    .8427579
           Corruption |  -.0164656   .0083668    -1.97   0.049    -.0328642    -.000067
              GDPpcln |  -.0876799   .1133298    -0.77   0.439    -.3098023    .1344424
         Market_Ageln |   .0854795   .1612608     0.53   0.596    -.2305858    .4015448
            Workersln |   .3546402    .020884    16.98   0.000     .3137082    .3955722
           Firm_Ageln |   .2087472   .0244633     8.53   0.000     .1608001    .2566943
     LocatedinCapital |   .1240285   .0686022     1.81   0.071    -.0104293    .2584863
     ForeignOwnership |   .0013072   .0009211     1.42   0.156    -.0004982    .0031126
        GovtOwnership |   .0005696   .0012031     0.47   0.636    -.0017884    .0029277
          SalestoGovt |   .0014239   .0012687     1.12   0.262    -.0010627    .0039106
       PubliclyListed |   .0605037   .1138245     0.53   0.595    -.1625883    .2835957
       DomesticInputs |  -.0031085   .0006971    -4.46   0.000    -.0044749   -.0017422
            Exporting |   .2877395   .0503222     5.72   0.000     .1891098    .3863691
                  MNC |   .1847236   .0816717     2.26   0.024     .0246501    .3447971
        Manufacturing |  -.5362226    .173193    -3.10   0.002    -.8756746   -.1967706
          Agriculture |  -.1521493   .2117577    -0.72   0.472    -.5671867    .2628882
         Construction |  -.4133497   .2000347    -2.07   0.039    -.8054105    -.021289
             Services |  -.3140154   .1658795    -1.89   0.058    -.6391332    .0111024
      EastAsiaPacific |  -1.341636   .5159817    -2.60   0.009    -2.352941   -.3303303
    EuropeCentralAsia |  -.8700235   .4384184    -1.98   0.047    -1.729308   -.0107392
LatinAmericaCaribbean |  -.7147816   .9440964    -0.76   0.449    -2.565176    1.135613
            SouthAsia |   .0814592   .7638325     0.11   0.915    -1.415625    1.578543
                      |
                 Year |
                2003  |  -.3343204   .3237809    -1.03   0.302    -.9689193    .3002784
                2004  |  -.0569772   .5847813    -0.10   0.922    -1.203127    1.089173
                2005  |  -.1455276   .1223823    -1.19   0.234    -.3853924    .0943372
                      |
                _cons |   .0236611   1.332739     0.02   0.986     -2.58846    2.635782
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3311682    .075644                      .2116513    .5181748
---------------------------------------------------------------------------------------

. *Table A4
. melogit BALobbies FHinverted Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwner
> ship GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Constructi
> on Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) v
> ce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10581.906  
Iteration 1:   log likelihood = -10467.328  
Iteration 2:   log likelihood = -10466.939  
Iteration 3:   log likelihood = -10466.939  

Refining starting values:

Grid node 0:   log likelihood = -9985.7816

Fitting full model:

Iteration 0:   log pseudolikelihood = -9985.7816  
Iteration 1:   log pseudolikelihood = -9979.9913  (backed up)
Iteration 2:   log pseudolikelihood = -9963.0494  
Iteration 3:   log pseudolikelihood = -9946.1829  
Iteration 4:   log pseudolikelihood = -9946.1199  
Iteration 5:   log pseudolikelihood = -9946.1198  

Mixed-effects logistic regression               Number of obs     =     24,146
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.7
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1152.63
Log pseudolikelihood = -9946.1198               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            BALobbies |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
           FHinverted |   .1054311   .0473998     2.22   0.026     .0125292     .198333
           Corruption |  -.0049077   .0124147    -0.40   0.693      -.02924    .0194247
              GDPpcln |   .1079244   .1513358     0.71   0.476    -.1886884    .4045372
         Market_Ageln |   .0995806   .1766535     0.56   0.573    -.2466539     .445815
            Workersln |   .3535057   .0212606    16.63   0.000     .3118358    .3951757
           Firm_Ageln |    .180082   .0300011     6.00   0.000     .1212808    .2388831
     LocatedinCapital |   .0918143   .0782163     1.17   0.240    -.0614869    .2451155
     ForeignOwnership |   .0013918   .0010994     1.27   0.206     -.000763    .0035466
        GovtOwnership |  -.0048563   .0012836    -3.78   0.000    -.0073721   -.0023404
          SalestoGovt |  -.0006807   .0011388    -0.60   0.550    -.0029127    .0015513
       PubliclyListed |   .2367358   .1344996     1.76   0.078    -.0268785    .5003501
       DomesticInputs |  -.0031091   .0008599    -3.62   0.000    -.0047946   -.0014237
            Exporting |   .2681524   .0592998     4.52   0.000     .1519269     .384378
                  MNC |   .1936105   .0810103     2.39   0.017     .0348333    .3523877
        Manufacturing |    -.27863   .1772319    -1.57   0.116    -.6259982    .0687381
          Agriculture |   .1492237   .2658224     0.56   0.575    -.3717786    .6702259
         Construction |  -.2011384   .1955539    -1.03   0.304     -.584417    .1821402
             Services |  -.2008713   .1754737    -1.14   0.252    -.5447934    .1430508
      EastAsiaPacific |  -2.552559    .987702    -2.58   0.010    -4.488419   -.6166985
    EuropeCentralAsia |  -1.462286   .5058913    -2.89   0.004    -2.453815   -.4707572
LatinAmericaCaribbean |  -1.678315   1.694434    -0.99   0.322    -4.999344    1.642715
            SouthAsia |  -.0319956   .9545401    -0.03   0.973     -1.90286    1.838868
                      |
                 Year |
                2003  |  -.0730497   .4095898    -0.18   0.858    -.8758309    .7297315
                2004  |   .5417867   .6916659     0.78   0.433    -.8138536    1.897427
                2005  |   .3403025   .2093727     1.63   0.104    -.0700604    .7506655
                      |
                _cons |  -3.455297   1.940018    -1.78   0.075    -7.257662    .3470677
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .5790644   .2175688                      .2772742    1.209329
---------------------------------------------------------------------------------------

. melogit BALobbies PolCon     Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwner
> ship GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Constructi
> on Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) v
> ce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10586.724  
Iteration 1:   log likelihood = -10480.989  
Iteration 2:   log likelihood =  -10480.76  
Iteration 3:   log likelihood =  -10480.76  

Refining starting values:

Grid node 0:   log likelihood = -9989.4069

Fitting full model:

Iteration 0:   log pseudolikelihood = -9989.4069  
Iteration 1:   log pseudolikelihood = -9983.2479  
Iteration 2:   log pseudolikelihood = -9965.9733  
Iteration 3:   log pseudolikelihood = -9948.4088  
Iteration 4:   log pseudolikelihood = -9948.3379  
Iteration 5:   log pseudolikelihood = -9948.3378  

Mixed-effects logistic regression               Number of obs     =     24,146
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.7
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1164.48
Log pseudolikelihood = -9948.3378               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            BALobbies |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               PolCon |   .7908432   .6560957     1.21   0.228    -.4950808    2.076767
           Corruption |  -.0078333   .0117449    -0.67   0.505     -.030853    .0151863
              GDPpcln |   .2723795    .165793     1.64   0.100    -.0525687    .5973278
         Market_Ageln |   .1425502   .1625826     0.88   0.381    -.1761058    .4612061
            Workersln |   .3532159   .0212515    16.62   0.000     .3115636    .3948682
           Firm_Ageln |   .1806277   .0300626     6.01   0.000     .1217061    .2395493
     LocatedinCapital |   .0923909   .0782601     1.18   0.238    -.0609961    .2457779
     ForeignOwnership |   .0013933   .0011005     1.27   0.205    -.0007635    .0035502
        GovtOwnership |  -.0048397   .0012857    -3.76   0.000    -.0073597   -.0023198
          SalestoGovt |   -.000732   .0011361    -0.64   0.519    -.0029587    .0014947
       PubliclyListed |   .2372392    .134667     1.76   0.078    -.0267033    .5011816
       DomesticInputs |  -.0031233   .0008629    -3.62   0.000    -.0048146   -.0014321
            Exporting |   .2688714   .0592653     4.54   0.000     .1527135    .3850294
                  MNC |   .1941779   .0811616     2.39   0.017      .035104    .3532518
        Manufacturing |  -.2768919   .1772887    -1.56   0.118    -.6243715    .0705876
          Agriculture |   .1520735   .2649266     0.57   0.566    -.3671731    .6713202
         Construction |  -.1994123   .1955626    -1.02   0.308     -.582708    .1838833
             Services |   -.198828   .1755758    -1.13   0.257    -.5429503    .1452944
      EastAsiaPacific |   -2.84565   1.113362    -2.56   0.011    -5.027799   -.6635013
    EuropeCentralAsia |  -1.659354   .5242541    -3.17   0.002    -2.686873   -.6318349
LatinAmericaCaribbean |  -1.788709   1.767263    -1.01   0.311    -5.252481    1.675062
            SouthAsia |  -.0535248    .915038    -0.06   0.953    -1.846966    1.739917
                      |
                 Year |
                2003  |   .1156134   .4594308     0.25   0.801    -.7848543    1.016081
                2004  |   .5657542    .662578     0.85   0.393    -.7328749    1.864383
                2005  |     .41104   .2432562     1.69   0.091    -.0657334    .8878134
                      |
                _cons |  -4.099142   2.061864    -1.99   0.047    -8.140321   -.0579621
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .6134682   .2413261                      .2837569    1.326287
---------------------------------------------------------------------------------------

. melogit BALobbies Polity     Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwner
> ship GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Constructi
> on Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) v
> ce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10572.306  
Iteration 1:   log likelihood = -10454.843  
Iteration 2:   log likelihood = -10454.306  
Iteration 3:   log likelihood = -10454.306  

Refining starting values:

Grid node 0:   log likelihood = -9985.2224

Fitting full model:

Iteration 0:   log pseudolikelihood = -9985.2224  
Iteration 1:   log pseudolikelihood = -9977.1482  
Iteration 2:   log pseudolikelihood = -9954.0428  
Iteration 3:   log pseudolikelihood = -9947.0215  
Iteration 4:   log pseudolikelihood = -9946.9912  
Iteration 5:   log pseudolikelihood = -9946.9911  

Mixed-effects logistic regression               Number of obs     =     24,146
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.7
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1082.71
Log pseudolikelihood = -9946.9911               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            BALobbies |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
               Polity |   .0443366   .0224987     1.97   0.049     .0002399    .0884333
           Corruption |  -.0101608   .0114439    -0.89   0.375    -.0325904    .0122688
              GDPpcln |   .1669327   .1691287     0.99   0.324    -.1645535    .4984189
         Market_Ageln |   .1155402   .1643933     0.70   0.482    -.2066647     .437745
            Workersln |    .353359   .0212401    16.64   0.000     .3117291    .3949889
           Firm_Ageln |   .1801823   .0300213     6.00   0.000     .1213417    .2390228
     LocatedinCapital |   .0919359   .0783055     1.17   0.240      -.06154    .2454119
     ForeignOwnership |   .0013974   .0010996     1.27   0.204    -.0007577    .0035525
        GovtOwnership |   -.004846    .001287    -3.77   0.000    -.0073685   -.0023236
          SalestoGovt |  -.0006858   .0011375    -0.60   0.547    -.0029152    .0015436
       PubliclyListed |   .2380224   .1345173     1.77   0.077    -.0256266    .5016715
       DomesticInputs |  -.0031147   .0008625    -3.61   0.000    -.0048051   -.0014243
            Exporting |   .2684632   .0593686     4.52   0.000     .1521028    .3848236
                  MNC |   .1930537   .0810429     2.38   0.017     .0342125     .351895
        Manufacturing |  -.2780635   .1773529    -1.57   0.117    -.6256689    .0695418
          Agriculture |   .1492791   .2654513     0.56   0.574    -.3709959     .669554
         Construction |  -.2003387   .1956892    -1.02   0.306    -.5838825    .1832051
             Services |  -.1999322   .1757396    -1.14   0.255    -.5443754    .1445111
      EastAsiaPacific |  -2.688004   1.066114    -2.52   0.012    -4.777548   -.5984588
    EuropeCentralAsia |  -1.558629   .5266554    -2.96   0.003    -2.590855   -.5264037
LatinAmericaCaribbean |  -1.822148   1.716179    -1.06   0.288    -5.185797      1.5415
            SouthAsia |  -.0087835   .9478076    -0.01   0.993    -1.866452    1.848885
                      |
                 Year |
                2003  |   .0162488   .4280141     0.04   0.970    -.8226433     .855141
                2004  |   .4749197   .6838864     0.69   0.487    -.8654729    1.815312
                2005  |   .3575668   .2139035     1.67   0.095    -.0616763    .7768098
                      |
                _cons |  -3.426985   1.977093    -1.73   0.083    -7.302015    .4480461
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .5895381   .2275325                       .276688    1.256127
---------------------------------------------------------------------------------------

. melogit BALobbies CGV        Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwner
> ship GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Constructi
> on Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) v
> ce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10551.996  
Iteration 1:   log likelihood =  -10418.44  
Iteration 2:   log likelihood = -10417.446  
Iteration 3:   log likelihood = -10417.445  

Refining starting values:

Grid node 0:   log likelihood = -9976.6304

Fitting full model:

Iteration 0:   log pseudolikelihood = -9976.6304  
Iteration 1:   log pseudolikelihood = -9969.9719  (backed up)
Iteration 2:   log pseudolikelihood = -9958.0086  
Iteration 3:   log pseudolikelihood = -9945.2652  
Iteration 4:   log pseudolikelihood = -9945.1463  
Iteration 5:   log pseudolikelihood = -9945.1461  

Mixed-effects logistic regression               Number of obs     =     24,146
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.7
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1133.41
Log pseudolikelihood = -9945.1461               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            BALobbies |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  CGV |   .7002306   .2461265     2.85   0.004     .2178315     1.18263
           Corruption |   -.006753   .0113166    -0.60   0.551    -.0289332    .0154272
              GDPpcln |   .2215517   .1416383     1.56   0.118    -.0560542    .4991577
         Market_Ageln |   .0861719   .1620718     0.53   0.595    -.2314829    .4038267
            Workersln |    .353618   .0212179    16.67   0.000     .3120317    .3952042
           Firm_Ageln |   .1800225   .0300886     5.98   0.000       .12105     .238995
     LocatedinCapital |    .090847   .0784642     1.16   0.247    -.0629399    .2446339
     ForeignOwnership |    .001395   .0010993     1.27   0.204    -.0007595    .0035496
        GovtOwnership |  -.0048532   .0012869    -3.77   0.000    -.0073756   -.0023309
          SalestoGovt |  -.0006751   .0011406    -0.59   0.554    -.0029107    .0015605
       PubliclyListed |   .2368246   .1346283     1.76   0.079    -.0270421    .5006912
       DomesticInputs |  -.0030997   .0008613    -3.60   0.000    -.0047879   -.0014116
            Exporting |     .26847    .059299     4.53   0.000     .1522461    .3846939
                  MNC |   .1931999      .0809     2.39   0.017     .0346388     .351761
        Manufacturing |  -.2791143    .177314    -1.57   0.115    -.6266433    .0684148
          Agriculture |   .1438915   .2671447     0.54   0.590    -.3797025    .6674855
         Construction |  -.2015564    .195729    -1.03   0.303    -.5851782    .1820654
             Services |  -.2005912   .1755935    -1.14   0.253    -.5447481    .1435657
      EastAsiaPacific |  -2.957106   1.055081    -2.80   0.005    -5.025028   -.8891849
    EuropeCentralAsia |   -1.90446   .4705862    -4.05   0.000    -2.826792   -.9821279
LatinAmericaCaribbean |  -2.226619   1.649116    -1.35   0.177    -5.458826    1.005589
            SouthAsia |  -.2838025   .9413553    -0.30   0.763    -2.128825     1.56122
                      |
                 Year |
                2003  |  -.0226922   .4274996    -0.05   0.958     -.860576    .8151917
                2004  |   .2925495   .7236345     0.40   0.686    -1.125748    1.710847
                2005  |    .335455   .2102348     1.60   0.111    -.0765977    .7475077
                      |
                _cons |  -3.488947   1.858931    -1.88   0.061    -7.132385    .1544912
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|     .56083   .2195145                      .2604129    1.207814
---------------------------------------------------------------------------------------

. melogit BALobbies BMR        Corruption GDPpcln Market_Ageln Workersln Firm_Ageln LocatedinCapital ForeignOwner
> ship GovtOwnership SalestoGovt PubliclyListed DomesticInputs Exporting MNC Manufacturing Agriculture Constructi
> on Services EastAsiaPacific EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) v
> ce(cluster Country)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10590.108  
Iteration 1:   log likelihood = -10479.352  
Iteration 2:   log likelihood = -10478.985  
Iteration 3:   log likelihood = -10478.985  

Refining starting values:

Grid node 0:   log likelihood = -9990.2571

Fitting full model:

Iteration 0:   log pseudolikelihood = -9990.2571  
Iteration 1:   log pseudolikelihood = -9983.6973  
Iteration 2:   log pseudolikelihood = -9966.2132  
Iteration 3:   log pseudolikelihood = -9947.6233  
Iteration 4:   log pseudolikelihood =  -9947.544  
Iteration 5:   log pseudolikelihood =  -9947.544  

Mixed-effects logistic regression               Number of obs     =     24,146
Group variable:     CountryYear                 Number of groups  =         76

                                                Obs per group:
                                                              min =         26
                                                              avg =      317.7
                                                              max =      1,379

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(25)     =    1145.97
Log pseudolikelihood =  -9947.544               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 45 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            BALobbies |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                  BMR |   .4989968   .2631786     1.90   0.058    -.0168238    1.014818
           Corruption |  -.0099427   .0110226    -0.90   0.367    -.0315466    .0116612
              GDPpcln |   .1874639   .1501919     1.25   0.212    -.1069069    .4818346
         Market_Ageln |   .1208677   .1576211     0.77   0.443     -.188064    .4297995
            Workersln |    .353302   .0212556    16.62   0.000     .3116419    .3949622
           Firm_Ageln |   .1805113   .0299643     6.02   0.000     .1217823    .2392403
     LocatedinCapital |    .092363   .0784328     1.18   0.239    -.0613626    .2460885
     ForeignOwnership |   .0013931   .0010994     1.27   0.205    -.0007618    .0035479
        GovtOwnership |  -.0048502    .001285    -3.77   0.000    -.0073687   -.0023316
          SalestoGovt |  -.0007188   .0011424    -0.63   0.529    -.0029579    .0015202
       PubliclyListed |   .2371223   .1346569     1.76   0.078    -.0268004    .5010451
       DomesticInputs |  -.0031134   .0008628    -3.61   0.000    -.0048043   -.0014224
            Exporting |   .2683582    .059298     4.53   0.000     .1521363    .3845801
                  MNC |    .193786   .0811707     2.39   0.017     .0346943    .3528777
        Manufacturing |  -.2788931   .1772645    -1.57   0.116    -.6263251    .0685388
          Agriculture |   .1506399    .266071     0.57   0.571    -.3708497    .6721295
         Construction |  -.2015501   .1956228    -1.03   0.303    -.5849639    .1818636
             Services |   -.201034   .1754535    -1.15   0.252    -.5449165    .1428485
      EastAsiaPacific |  -2.860053    1.09699    -2.61   0.009    -5.010113   -.7099928
    EuropeCentralAsia |  -1.676819    .533027    -3.15   0.002    -2.721533   -.6321051
LatinAmericaCaribbean |  -2.000781   1.705135    -1.17   0.241    -5.342784    1.341221
            SouthAsia |  -.2052822   .9190547    -0.22   0.823    -2.006596    1.596032
                      |
                 Year |
                2003  |  -.0442716   .4362315    -0.10   0.919    -.8992697    .8107265
                2004  |   .3737709   .6831067     0.55   0.584    -.9650936    1.712635
                2005  |   .3457747   .2162376     1.60   0.110    -.0780432    .7695926
                      |
                _cons |  -3.165783   1.853072    -1.71   0.088    -6.797737    .4661716
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .6048875   .2357347                      .2818041     1.29838
---------------------------------------------------------------------------------------

. *Table A5
. meologit Influence Lobby LobbyXFHinverted FHinverted Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific 
> EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
note: 2005.Year identifies no observations in the sample

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10102.363  
Iteration 1:   log likelihood = -9091.4613  
Iteration 2:   log likelihood = -8990.0625  
Iteration 3:   log likelihood = -8989.7457  
Iteration 4:   log likelihood = -8989.7457  

Refining starting values:

Grid node 0:   log likelihood = -8774.0509

Fitting full model:

Iteration 0:   log pseudolikelihood = -8774.0509  (not concave)
Iteration 1:   log pseudolikelihood = -8768.4747  
Iteration 2:   log pseudolikelihood = -8764.0813  
Iteration 3:   log pseudolikelihood = -8762.1135  
Iteration 4:   log pseudolikelihood = -8762.0425  
Iteration 5:   log pseudolikelihood = -8762.0422  

Mixed-effects ologit regression                 Number of obs     =     11,454
Group variable:     CountryYear                 Number of groups  =         44

                                                Obs per group:
                                                              min =         46
                                                              avg =      260.3
                                                              max =      1,147

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(13)     =   61635.39
Log pseudolikelihood = -8762.0422               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 39 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            Influence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                Lobby |   1.913428   .4749051     4.03   0.000     .9826308    2.844224
     LobbyXFHinverted |  -.0025519    .053415    -0.05   0.962    -.1072433    .1021395
           FHinverted |   .1083333    .040019     2.71   0.007     .0298976    .1867691
           Corruption |    .009233   .0111139     0.83   0.406    -.0125498    .0310159
              GDPpcln |  -.2184117   .1831706    -1.19   0.233    -.5774194     .140596
         Market_Ageln |  -.2122666   .1173053    -1.81   0.070    -.4421807    .0176475
            Workersln |   .1438224   .0204765     7.02   0.000     .1036891    .1839556
      EastAsiaPacific |    .279486   .6204864     0.45   0.652     -.936645    1.495617
    EuropeCentralAsia |  -1.250445   .5885819    -2.12   0.034    -2.404044   -.0968456
LatinAmericaCaribbean |   .0071568   .8682821     0.01   0.993    -1.694645    1.708958
            SouthAsia |  -.4721907   .5419025    -0.87   0.384      -1.5343    .5899186
                      |
                 Year |
                2003  |  -.5373983   .2269889    -2.37   0.018    -.9822884   -.0925082
                2004  |  -.7144328   .1366032    -5.23   0.000    -.9821702   -.4466954
                2005  |          0  (empty)
----------------------+----------------------------------------------------------------
                /cut1 |   .0174876   1.879726                     -3.666707    3.701683
                /cut2 |    1.30809   1.870969                     -2.358943    4.975122
                /cut3 |    2.41909   1.863776                     -1.233844    6.072023
                /cut4 |   3.824179   1.869589                      .1598514    7.488506
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3828212   .1162495                      .2111132    .6941868
---------------------------------------------------------------------------------------

. meologit Influence Lobby LobbyXPolCon PolCon         Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific 
> EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
note: 2005.Year identifies no observations in the sample

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10102.363  
Iteration 1:   log likelihood = -9094.3081  
Iteration 2:   log likelihood = -8989.5628  
Iteration 3:   log likelihood = -8989.2647  
Iteration 4:   log likelihood = -8989.2647  

Refining starting values:

Grid node 0:   log likelihood = -8766.9524

Fitting full model:

Iteration 0:   log pseudolikelihood = -8766.9524  (not concave)
Iteration 1:   log pseudolikelihood = -8761.4993  
Iteration 2:   log pseudolikelihood = -8757.0064  
Iteration 3:   log pseudolikelihood = -8755.7426  
Iteration 4:   log pseudolikelihood = -8755.7395  
Iteration 5:   log pseudolikelihood = -8755.7395  

Mixed-effects ologit regression                 Number of obs     =     11,454
Group variable:     CountryYear                 Number of groups  =         44

                                                Obs per group:
                                                              min =         46
                                                              avg =      260.3
                                                              max =      1,147

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(13)     =   36001.14
Log pseudolikelihood = -8755.7395               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 39 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            Influence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                Lobby |   2.150725   .2968643     7.24   0.000     1.568882    2.732569
         LobbyXPolCon |   -.849546   .7048857    -1.21   0.228    -2.231096    .5320046
               PolCon |   1.287949   .6619681     1.95   0.052    -.0094841    2.585383
           Corruption |   .0085108   .0117488     0.72   0.469    -.0145165    .0315381
              GDPpcln |  -.0013631   .1651623    -0.01   0.993    -.3250753    .3223492
         Market_Ageln |  -.2044916   .1317501    -1.55   0.121     -.462717    .0537339
            Workersln |   .1481131   .0198904     7.45   0.000     .1091287    .1870975
      EastAsiaPacific |  -.1034358   .6183229    -0.17   0.867    -1.315327    1.108455
    EuropeCentralAsia |  -1.587876   .6051221    -2.62   0.009    -2.773893   -.4018584
LatinAmericaCaribbean |  -.2087947   .8448429    -0.25   0.805    -1.864656    1.447067
            SouthAsia |  -.5675833   .6023607    -0.94   0.346    -1.748189    .6130219
                      |
                 Year |
                2003  |  -.3228039   .2270593    -1.42   0.155     -.767832    .1222242
                2004  |  -.6046458   .1347957    -4.49   0.000    -.8688406   -.3404511
                2005  |          0  (empty)
----------------------+----------------------------------------------------------------
                /cut1 |   1.154215   1.805831                     -2.385149    4.693578
                /cut2 |    2.44744    1.78615                      -1.05335    5.948229
                /cut3 |   3.561316   1.780524                      .0715538    7.051079
                /cut4 |   4.968608   1.785032                       1.47001    8.467207
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .3977408   .1174896                      .2229265    .7096408
---------------------------------------------------------------------------------------

. meologit Influence Lobby LobbyXPolity Polity         Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific 
> EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
note: 2005.Year identifies no observations in the sample

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10102.363  
Iteration 1:   log likelihood = -9112.7108  
Iteration 2:   log likelihood = -9013.3692  
Iteration 3:   log likelihood = -9013.0978  
Iteration 4:   log likelihood = -9013.0977  

Refining starting values:

Grid node 0:   log likelihood = -8773.4563

Fitting full model:

Iteration 0:   log pseudolikelihood = -8773.4563  (not concave)
Iteration 1:   log pseudolikelihood = -8768.1578  
Iteration 2:   log pseudolikelihood =  -8763.347  
Iteration 3:   log pseudolikelihood = -8761.9758  
Iteration 4:   log pseudolikelihood =  -8761.975  

Mixed-effects ologit regression                 Number of obs     =     11,454
Group variable:     CountryYear                 Number of groups  =         44

                                                Obs per group:
                                                              min =         46
                                                              avg =      260.3
                                                              max =      1,147

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(13)     =   62782.21
Log pseudolikelihood =  -8761.975               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 39 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            Influence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                Lobby |   2.089558   .4485247     4.66   0.000     1.210466    2.968651
         LobbyXPolity |  -.0145892   .0263547    -0.55   0.580    -.0662434     .037065
               Polity |   .0331507   .0226139     1.47   0.143    -.0111717    .0774732
           Corruption |   .0035343   .0128931     0.27   0.784    -.0217356    .0288042
              GDPpcln |  -.0885245   .1924559    -0.46   0.646     -.465731    .2886821
         Market_Ageln |  -.1616291   .1244314    -1.30   0.194    -.4055102    .0822521
            Workersln |   .1454077   .0199877     7.27   0.000     .1062325     .184583
      EastAsiaPacific |  -.0480144   .6284825    -0.08   0.939    -1.279817    1.183789
    EuropeCentralAsia |  -1.404834   .6139367    -2.29   0.022    -2.608128     -.20154
LatinAmericaCaribbean |   -.211076   .8937779    -0.24   0.813    -1.962848    1.540696
            SouthAsia |  -.6458446   .5813056    -1.11   0.267    -1.785183    .4934933
                      |
                 Year |
                2003  |  -.4052365   .2337527    -1.73   0.083    -.8633833    .0529103
                2004  |   -.617164   .1748057    -3.53   0.000    -.9597769    -.274551
                2005  |          0  (empty)
----------------------+----------------------------------------------------------------
                /cut1 |   .3925876   2.033218                     -3.592447    4.377622
                /cut2 |   1.684314   2.025616                     -2.285821    5.654448
                /cut3 |   2.796364   2.017974                     -1.158793     6.75152
                /cut4 |   4.202075   2.021573                       .239864    8.164285
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|    .413686   .1179652                      .2365621    .7234299
---------------------------------------------------------------------------------------

. meologit Influence Lobby LobbyXCGV CGV               Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific 
> EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
note: 2005.Year identifies no observations in the sample

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10102.363  
Iteration 1:   log likelihood = -9132.7364  
Iteration 2:   log likelihood = -9034.4272  
Iteration 3:   log likelihood = -9034.1677  
Iteration 4:   log likelihood = -9034.1676  

Refining starting values:

Grid node 0:   log likelihood = -8778.4787

Fitting full model:

Iteration 0:   log pseudolikelihood = -8778.4787  (not concave)
Iteration 1:   log pseudolikelihood = -8773.4663  
Iteration 2:   log pseudolikelihood = -8766.4204  
Iteration 3:   log pseudolikelihood = -8764.2964  
Iteration 4:   log pseudolikelihood = -8764.2948  
Iteration 5:   log pseudolikelihood = -8764.2948  

Mixed-effects ologit regression                 Number of obs     =     11,454
Group variable:     CountryYear                 Number of groups  =         44

                                                Obs per group:
                                                              min =         46
                                                              avg =      260.3
                                                              max =      1,147

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(13)     =   47748.95
Log pseudolikelihood = -8764.2948               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 39 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            Influence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                Lobby |   1.907433   .3667907     5.20   0.000     1.188536     2.62633
            LobbyXCGV |  -.0165598   .4136191    -0.04   0.968    -.8272384    .7941188
                  CGV |   .0880053   .3731819     0.24   0.814    -.6434178    .8194283
           Corruption |   .0046369   .0137544     0.34   0.736    -.0223212    .0315949
              GDPpcln |    .002677   .2044257     0.01   0.990    -.3979899     .403344
         Market_Ageln |  -.1304879   .1278453    -1.02   0.307    -.3810601    .1200843
            Workersln |   .1434074   .0206367     6.95   0.000     .1029603    .1838546
      EastAsiaPacific |  -.1665118   .6173183    -0.27   0.787    -1.376434     1.04341
    EuropeCentralAsia |  -1.488748   .6051486    -2.46   0.014    -2.674818   -.3026784
LatinAmericaCaribbean |  -.2811659   .9321583    -0.30   0.763    -2.108163    1.545831
            SouthAsia |  -.8410646   .5397882    -1.56   0.119     -1.89903    .2169009
                      |
                 Year |
                2003  |  -.3727219   .2415774    -1.54   0.123    -.8462049    .1007611
                2004  |  -.4916229   .2675475    -1.84   0.066    -1.016006    .0327607
                2005  |          0  (empty)
----------------------+----------------------------------------------------------------
                /cut1 |   .8031077   2.205288                     -3.519176    5.125392
                /cut2 |   2.093905   2.196136                     -2.210442    6.398253
                /cut3 |   3.205162   2.189245                      -1.08568    7.496005
                /cut4 |   4.610528   2.187939                      .3222468     8.89881
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .4282285   .1096257                      .2592804     .707264
---------------------------------------------------------------------------------------

. meologit Influence Lobby LobbyXBMR BMR               Corruption GDPpcln Market_Ageln Workersln EastAsiaPacific 
> EuropeCentralAsia LatinAmericaCaribbean SouthAsia i.Year || CountryYear:, cov(un) vce(cluster Country)
note: 2005.Year identifies no observations in the sample

Fitting fixed-effects model:

Iteration 0:   log likelihood = -10102.363  
Iteration 1:   log likelihood = -9132.1721  
Iteration 2:   log likelihood = -9033.8697  
Iteration 3:   log likelihood = -9033.6119  
Iteration 4:   log likelihood = -9033.6119  

Refining starting values:

Grid node 0:   log likelihood = -8777.9947

Fitting full model:

Iteration 0:   log pseudolikelihood = -8777.9947  (not concave)
Iteration 1:   log pseudolikelihood = -8772.9688  
Iteration 2:   log pseudolikelihood = -8765.8402  
Iteration 3:   log pseudolikelihood = -8763.6716  
Iteration 4:   log pseudolikelihood = -8763.6701  
Iteration 5:   log pseudolikelihood = -8763.6701  

Mixed-effects ologit regression                 Number of obs     =     11,454
Group variable:     CountryYear                 Number of groups  =         44

                                                Obs per group:
                                                              min =         46
                                                              avg =      260.3
                                                              max =      1,147

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(13)     =   45332.37
Log pseudolikelihood = -8763.6701               Prob > chi2       =     0.0000
                                        (Std. Err. adjusted for 39 clusters in Country)
---------------------------------------------------------------------------------------
                      |               Robust
            Influence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                Lobby |   1.958943   .3539142     5.54   0.000     1.265284    2.652602
            LobbyXBMR |  -.1107033   .4027854    -0.27   0.783    -.9001481    .6787416
                  BMR |   .1698698    .382612     0.44   0.657     -.580036    .9197755
           Corruption |   .0045218    .013695     0.33   0.741      -.02232    .0313636
              GDPpcln |  -.0156338   .2106644    -0.07   0.941    -.4285284    .3972608
         Market_Ageln |  -.1381151   .1305062    -1.06   0.290    -.3939026    .1176724
            Workersln |   .1442457   .0207528     6.95   0.000      .103571    .1849205
      EastAsiaPacific |  -.1405993   .6234002    -0.23   0.822    -1.362441    1.081243
    EuropeCentralAsia |  -1.476506   .6078905    -2.43   0.015    -2.667949   -.2850622
LatinAmericaCaribbean |  -.2783074   .9253157    -0.30   0.764    -2.091893    1.535278
            SouthAsia |  -.8230772   .5446593    -1.51   0.131     -1.89059    .2444354
                      |
                 Year |
                2003  |  -.3892516   .2454361    -1.59   0.113    -.8702976    .0917944
                2004  |  -.5220276   .2694701    -1.94   0.053    -1.050179    .0061239
                2005  |          0  (empty)
----------------------+----------------------------------------------------------------
                /cut1 |   .6818004   2.270694                     -3.768679    5.132279
                /cut2 |   1.973058   2.264098                     -2.464492    6.410608
                /cut3 |   3.084757   2.257648                     -1.340153    7.509666
                /cut4 |   4.490375   2.256641                      .0674399     8.91331
----------------------+----------------------------------------------------------------
CountryYear           |
            var(_cons)|   .4293651   .1117938                      .2577504    .7152438
---------------------------------------------------------------------------------------

. *fn#21
. pwcorr Exporting Manufacturing Services Agriculture Construction Other, sig

             | Export~g Manufa~g Services Agricu~e Constr~n    Other
-------------+------------------------------------------------------
   Exporting |   1.0000 
             |
             |
Manufactur~g |   0.2227   1.0000 
             |   0.0000
             |
    Services |  -0.1779  -0.7888   1.0000 
             |   0.0000   0.0000
             |
 Agriculture |   0.0141  -0.1323  -0.1243   1.0000 
             |   0.0168   0.0000   0.0000
             |
Construction |  -0.0939  -0.2855  -0.2683  -0.0450   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
       Other |   0.0182  -0.0874  -0.0821  -0.0138  -0.0297   1.0000 
             |   0.0020   0.0000   0.0000   0.0196   0.0000
             |

. *Table 5 includes a set of t-tests.  Note that our tables subtract group 0 (Non-democracy) from group 1 (Democr
> acy) intead of the reverse as done by Stata
. ttest Exporting        if Lobby==1, by (Polity01)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457     .273164    .0116775    .4457374    .2502575    .2960705
       1 |   3,057    .3006215    .0082945    .4586033    .2843582    .3168849
---------+--------------------------------------------------------------------
combined |   4,514     .291759    .0067666    .4546223    .2784931    .3050248
---------+--------------------------------------------------------------------
    diff |           -.0274575    .0144687               -.0558232    .0009082
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.8977
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0289         Pr(|T| > |t|) = 0.0578          Pr(T > t) = 0.9711

. ttest Import_Competing if Lobby==1, by (Polity01)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457      .42965    .0129732    .4951961    .4042018    .4550981
       1 |   3,057    .2394504    .0077196    .4268179    .2243143    .2545866
---------+--------------------------------------------------------------------
combined |   4,514    .3008418    .0068269    .4586749    .2874577    .3142259
---------+--------------------------------------------------------------------
    diff |            .1901995    .0143263                .1621129    .2182862
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  13.2762
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest Nontradable      if Lobby==1, by (Polity01)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457     .297186    .0119772     .457176    .2736917    .3206803
       1 |   3,057     .459928    .0090156    .4984732    .4422508    .4776053
---------+--------------------------------------------------------------------
combined |   4,514    .4073992    .0073141    .4914047    .3930601    .4217383
---------+--------------------------------------------------------------------
    diff |            -.162742    .0154568                -.193045   -.1324391
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -10.5288
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest Exporting        if Lobby==1, by (CGV)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457    .2621826    .0115265    .4399724    .2395723    .2847928
       1 |   3,057    .3058554     .008335    .4608442    .2895126    .3221982
---------+--------------------------------------------------------------------
combined |   4,514     .291759    .0067666    .4546223    .2784931    .3050248
---------+--------------------------------------------------------------------
    diff |           -.0436728    .0144598               -.0720212   -.0153245
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.0203
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0013         Pr(|T| > |t|) = 0.0025          Pr(T > t) = 0.9987

. ttest Import_Competing if Lobby==1, by (CGV)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457    .4179822    .0129261    .4933966    .3926265    .4433379
       1 |   3,057    .2450114    .0077801    .4301644    .2297566    .2602663
---------+--------------------------------------------------------------------
combined |   4,514    .3008418    .0068269    .4586749    .2874577    .3142259
---------+--------------------------------------------------------------------
    diff |            .1729707    .0143747                .1447893    .2011521
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  12.0330
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest Nontradable      if Lobby==1, by (CGV)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457    .3198353    .0122233    .4665727    .2958581    .3438125
       1 |   3,057    .4491331    .0089978    .4974872    .4314909    .4667754
---------+--------------------------------------------------------------------
combined |   4,514    .4073992    .0073141    .4914047    .3930601    .4217383
---------+--------------------------------------------------------------------
    diff |           -.1292979    .0155267               -.1597378    -.098858
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -8.3275
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest Exporting        if Lobby==1, by (BMR)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,404    .2428775    .0114485    .4289745    .2204195    .2653355
       1 |   3,110    .3138264    .0083225    .4641214    .2975083    .3301444
---------+--------------------------------------------------------------------
combined |   4,514     .291759    .0067666    .4546223    .2784931    .3050248
---------+--------------------------------------------------------------------
    diff |           -.0709489    .0145807               -.0995343   -.0423635
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -4.8659
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest Import_Competing if Lobby==1, by (BMR)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,404    .4152422    .0131556    .4929393    .3894354    .4410489
       1 |   3,110    .2491961    .0077575    .4326172    .2339857    .2644065
---------+--------------------------------------------------------------------
combined |   4,514    .3008418    .0068269    .4586749    .2874577    .3142259
---------+--------------------------------------------------------------------
    diff |             .166046    .0145406                .1375393    .1945528
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  11.4195
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest Nontradable      if Lobby==1, by (BMR)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,404    .3418803    .0126637    .4745087    .3170385    .3667222
       1 |   3,110    .4369775    .0088957     .496092    .4195354    .4544196
---------+--------------------------------------------------------------------
combined |   4,514    .4073992    .0073141    .4914047    .3930601    .4217383
---------+--------------------------------------------------------------------
    diff |           -.0950972    .0157382               -.1259517   -.0642426
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -6.0425
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. *fn#26
. ttest MNC if Lobby==1, by (Polity01)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457    .1015786     .007917     .302197    .0860486    .1171085
       1 |   3,057    .1475303    .0064151    .3546917    .1349519    .1601086
---------+--------------------------------------------------------------------
combined |   4,514    .1326983    .0050499    .3392859    .1227979    .1425986
---------+--------------------------------------------------------------------
    diff |           -.0459517    .0107806                -.067087   -.0248163
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -4.2624
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest MNC if Lobby==1, by (CGV)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,457    .1070693    .0081033    .3093075     .091174    .1229647
       1 |   3,057    .1449133    .0063677     .352071    .1324279    .1573987
---------+--------------------------------------------------------------------
combined |   4,514    .1326983    .0050499    .3392859    .1227979    .1425986
---------+--------------------------------------------------------------------
    diff |            -.037844    .0107876                -.058993    -.016695
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.5081
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0002         Pr(|T| > |t|) = 0.0005          Pr(T > t) = 0.9998

. ttest MNC if Lobby==1, by (BMR)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   1,404    .0940171    .0077917    .2919565    .0787324    .1093018
       1 |   3,110    .1501608    .0064067    .3572864    .1375989    .1627226
---------+--------------------------------------------------------------------
combined |   4,514    .1326983    .0050499    .3392859    .1227979    .1425986
---------+--------------------------------------------------------------------
    diff |           -.0561437    .0108781               -.0774701   -.0348173
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -5.1612
Ho: diff = 0                                     degrees of freedom =     4512

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. *Table 6 includes a set of t-tests.  Note that our tables subtract group 0 (Non-democracy) from group 1 (Democr
> acy) intead of the reverse as done by Stata
. ttest Exporting        if LobbyBA==1, by (Polity01)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,403    .2704952    .0090637    .4443081    .2527217    .2882688
       1 |   6,505    .2843966    .0055938    .4511612    .2734309    .2953623
---------+--------------------------------------------------------------------
combined |   8,908    .2806466    .0047609    .4493404    .2713142     .289979
---------+--------------------------------------------------------------------
    diff |           -.0139014    .0107263               -.0349273    .0071245
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.2960
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0975         Pr(|T| > |t|) = 0.1950          Pr(T > t) = 0.9025

. ttest Import_Competing if LobbyBA==1, by (Polity01)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,403    .4931336     .010201    .5000569    .4731299    .5131372
       1 |   6,505    .2558032    .0054101    .4363453    .2451976    .2664088
---------+--------------------------------------------------------------------
combined |   8,908    .3198249     .004942    .4664347    .3101375    .3295123
---------+--------------------------------------------------------------------
    diff |            .2373304    .0108477                .2160664    .2585943
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  21.8784
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest Nontradable      if LobbyBA==1, by (Polity01)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,403    .2363712    .0086687    .4249412    .2193724      .25337
       1 |   6,505    .4598002    .0061798    .4984197    .4476858    .4719145
---------+--------------------------------------------------------------------
combined |   8,908    .3995285    .0051898     .489829    .3893552    .4097018
---------+--------------------------------------------------------------------
    diff |            -.223429    .0114517               -.2458769    -.200981
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -19.5105
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest Exporting        if LobbyBA==1, by (CGV)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,382    .2565071    .0089497    .4367967    .2389571    .2740571
       1 |   6,526    .2894576    .0056143    .4535454    .2784516    .3004635
---------+--------------------------------------------------------------------
combined |   8,908    .2806466    .0047609    .4493404    .2713142     .289979
---------+--------------------------------------------------------------------
    diff |           -.0329504    .0107514               -.0540257   -.0118751
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.0647
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0011         Pr(|T| > |t|) = 0.0022          Pr(T > t) = 0.9989

. ttest Import_Competing if LobbyBA==1, by (CGV)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,382    .4785894    .0102374    .4996463    .4585142    .4986647
       1 |   6,526    .2618756    .0054428     .439689    .2512059    .2725452
---------+--------------------------------------------------------------------
combined |   8,908    .3198249     .004942    .4664347    .3101375    .3295123
---------+--------------------------------------------------------------------
    diff |            .2167138    .0109277                .1952931    .2381346
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  19.8317
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest Nontradable      if LobbyBA==1, by (CGV)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,382    .2649034    .0090435    .4413744    .2471695    .2826374
       1 |   6,526    .4486669    .0061571     .497396    .4365969    .4607369
---------+--------------------------------------------------------------------
combined |   8,908    .3995285    .0051898     .489829    .3893552    .4097018
---------+--------------------------------------------------------------------
    diff |           -.1837634    .0115636               -.2064307   -.1610961
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -15.8916
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest Exporting        if LobbyBA==1, by (BMR)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,245    .2351893    .0089531    .4242116     .217632    .2527466
       1 |   6,663    .2959628    .0055926    .4565086    .2849995    .3069261
---------+--------------------------------------------------------------------
combined |   8,908    .2806466    .0047609    .4493404    .2713142     .289979
---------+--------------------------------------------------------------------
    diff |           -.0607735     .010947               -.0822322   -.0393148
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -5.5516
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest Import_Competing if LobbyBA==1, by (BMR)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,245     .477951    .0105447    .4996249    .4572725    .4986295
       1 |   6,663    .2665466    .0054171    .4421864    .2559273    .2771659
---------+--------------------------------------------------------------------
combined |   8,908    .3198249     .004942    .4664347    .3101375    .3295123
---------+--------------------------------------------------------------------
    diff |            .2114044    .0111605                .1895272    .2332816
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  18.9421
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest Nontradable      if LobbyBA==1, by (BMR)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,245    .2868597     .009548    .4523963    .2681359    .3055835
       1 |   6,663    .4374906    .0060778    .4961144    .4255762    .4494051
---------+--------------------------------------------------------------------
combined |   8,908    .3995285    .0051898     .489829    .3893552    .4097018
---------+--------------------------------------------------------------------
    diff |           -.1506309     .011847               -.1738538    -.127408
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -12.7147
Ho: diff = 0                                     degrees of freedom =     8906

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/davidbearce/log/PSRMlogfile.log
  log type:  text
 closed on:  17 Oct 2021, 20:02:41
-----------------------------------------------------------------------------------------------------------------
